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Bonomo M, Rombo SE. Neighborhood based computational approaches for the prediction of lncRNA-disease associations. BMC Bioinformatics 2024; 25:187. [PMID: 38741200 DOI: 10.1186/s12859-024-05777-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 04/11/2024] [Indexed: 05/16/2024] Open
Abstract
MOTIVATION Long non-coding RNAs (lncRNAs) are a class of molecules involved in important biological processes. Extensive efforts have been provided to get deeper understanding of disease mechanisms at the lncRNA level, guiding towards the detection of biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of disease-lncRNA associations allow to identify the most promising candidates to be verified in laboratory, reducing costs and time consuming. RESULTS We propose novel approaches for the prediction of lncRNA-disease associations, all sharing the idea of exploring associations among lncRNAs, other intermediate molecules (e.g., miRNAs) and diseases, suitably represented by tripartite graphs. Indeed, while only a few lncRNA-disease associations are still known, plenty of interactions between lncRNAs and other molecules, as well as associations of the latters with diseases, are available. A first approach presented here, NGH, relies on neighborhood analysis performed on a tripartite graph, built upon lncRNAs, miRNAs and diseases. A second approach (CF) relies on collaborative filtering; a third approach (NGH-CF) is obtained boosting NGH by collaborative filtering. The proposed approaches have been validated on both synthetic and real data, and compared against other methods from the literature. It results that neighborhood analysis allows to outperform competitors, and when it is combined with collaborative filtering the prediction accuracy further improves, scoring a value of AUC equal to 0966. AVAILABILITY Source code and sample datasets are available at: https://github.com/marybonomo/LDAsPredictionApproaches.git.
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Affiliation(s)
| | - Simona E Rombo
- Kazaam Lab s.r.l., Palermo, Italy
- Department of Mathematics and Computer Science, University of Palermo, Palermo, Italy
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Peng L, Ren M, Huang L, Chen M. GEnDDn: An lncRNA-Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network. Interdiscip Sci 2024:10.1007/s12539-024-00619-w. [PMID: 38733474 DOI: 10.1007/s12539-024-00619-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Revised: 02/02/2024] [Accepted: 02/03/2024] [Indexed: 05/13/2024]
Abstract
Accumulating studies have demonstrated close relationships between long non-coding RNAs (lncRNAs) and diseases. Identification of new lncRNA-disease associations (LDAs) enables us to better understand disease mechanisms and further provides promising insights into cancer targeted therapy and anti-cancer drug design. Here, we present an LDA prediction framework called GEnDDn based on deep learning. GEnDDn mainly comprises two steps: First, features of both lncRNAs and diseases are extracted by combining similarity computation, non-negative matrix factorization, and graph attention auto-encoder, respectively. And each lncRNA-disease pair (LDP) is depicted as a vector based on concatenation operation on the extracted features. Subsequently, unknown LDPs are classified by aggregating dual-net neural architecture and deep neural network. Using six different evaluation metrics, we found that GEnDDn surpassed four competing LDA identification methods (SDLDA, LDNFSGB, IPCARF, LDASR) on the lncRNADisease and MNDR databases under fivefold cross-validation experiments on lncRNAs, diseases, LDPs, and independent lncRNAs and independent diseases, respectively. Ablation experiments further validated the powerful LDA prediction performance of GEnDDn. Furthermore, we utilized GEnDDn to find underlying lncRNAs for lung cancer and breast cancer. The results elucidated that there may be dense linkages between IFNG-AS1 and lung cancer as well as between HIF1A-AS1 and breast cancer. The results require further biomedical experimental verification. GEnDDn is publicly available at https://github.com/plhhnu/GEnDDn.
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Affiliation(s)
- Lihong Peng
- College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, 412007, China
| | - Mengnan Ren
- College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, 412007, China
| | - Liangliang Huang
- College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou, 412007, China
| | - Min Chen
- School of Computer Science, Hunan Institute of Technology, Hengyang, 421002, China.
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Zhou L, Peng X, Zeng L, Peng L. Finding potential lncRNA-disease associations using a boosting-based ensemble learning model. Front Genet 2024; 15:1356205. [PMID: 38495672 PMCID: PMC10940470 DOI: 10.3389/fgene.2024.1356205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 02/01/2024] [Indexed: 03/19/2024] Open
Abstract
Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious. Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA-disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA-disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network. Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively. Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification.
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Affiliation(s)
- Liqian Zhou
- School of Computer Science, Hunan University of Technology, Zhuzhou, Hunan, China
| | - Xinhuai Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, Hunan, China
| | - Lijun Zeng
- School of Computer Science, Hunan Institute of Technology, Hengyang, China
| | - Lihong Peng
- School of Computer Science, Hunan University of Technology, Zhuzhou, Hunan, China
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Su Z, Lu H, Wu Y, Li Z, Duan L. Predicting potential lncRNA biomarkers for lung cancer and neuroblastoma based on an ensemble of a deep neural network and LightGBM. Front Genet 2023; 14:1238095. [PMID: 37655066 PMCID: PMC10466784 DOI: 10.3389/fgene.2023.1238095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2023] [Accepted: 07/19/2023] [Indexed: 09/02/2023] Open
Abstract
Introduction: Lung cancer is one of the most frequent neoplasms worldwide with approximately 2.2 million new cases and 1.8 million deaths each year. The expression levels of programmed death ligand-1 (PDL1) demonstrate a complex association with lung cancer. Neuroblastoma is a high-risk malignant tumor and is mainly involved in childhood patients. Identification of new biomarkers for these two diseases can significantly promote their diagnosis and therapy. However, in vivo experiments to discover potential biomarkers are costly and laborious. Consequently, artificial intelligence technologies, especially machine learning methods, provide a powerful avenue to find new biomarkers for various diseases. Methods: We developed a machine learning-based method named LDAenDL to detect potential long noncoding RNA (lncRNA) biomarkers for lung cancer and neuroblastoma using an ensemble of a deep neural network and LightGBM. LDAenDL first computes the Gaussian kernel similarity and functional similarity of lncRNAs and the Gaussian kernel similarity and semantic similarity of diseases to obtain their similar networks. Next, LDAenDL combines a graph convolutional network, graph attention network, and convolutional neural network to learn the biological features of the lncRNAs and diseases based on their similarity networks. Third, these features are concatenated and fed to an ensemble model composed of a deep neural network and LightGBM to find new lncRNA-disease associations (LDAs). Finally, the proposed LDAenDL method is applied to identify possible lncRNA biomarkers associated with lung cancer and neuroblastoma. Results: The experimental results show that LDAenDL computed the best AUCs of 0.8701, 107 0.8953, and 0.9110 under cross-validation on lncRNAs, diseases, and lncRNA-disease pairs on Dataset 1, respectively, and 0.9490, 0.9157, and 0.9708 on Dataset 2, respectively. Furthermore, AUPRs of 0.8903, 0.9061, and 0.9166 under three cross-validations were obtained on Dataset 1, and 0.9582, 0.9122, and 0.9743 on Dataset 2. The results demonstrate that LDAenDL significantly outperformed the other four classical LDA prediction methods (i.e., SDLDA, LDNFSGB, IPCAF, and LDASR). Case studies demonstrate that CCDC26 and IFNG-AS1 may be new biomarkers of lung cancer, SNHG3 may associate with PDL1 for lung cancer, and HOTAIR and BDNF-AS may be potential biomarkers of neuroblastoma. Conclusion: We hope that the proposed LDAenDL method can help the development of targeted therapies for these two diseases.
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Affiliation(s)
- Zhenguo Su
- Clinical Lab, Yantai Affiliated Hospital of Binzhou Medical University, Yantai, China
| | - Huihui Lu
- Department of Thoracic Cardiovascular Surgery, Hunan Province Directly Affiliated TCM Hospital, Zhuzhou, China
| | - Yan Wu
- Geneis (Beijing) Co., Ltd., Beijing, China
| | - Zejun Li
- School of Computer Science, Hunan Institute of Technology, Hengyang, China
| | - Lian Duan
- Faculty of Pediatrics, The Chinese PLA General Hospital, Beijing, China
- Department of Pediatric Surgery, The Seventh Medical Center of PLA General Hospital, Beijing, China
- National Engineering Laboratory for Birth Defects Prevention and Control of Key Technology, Beijing, China
- Beijing Key Laboratory of Pediatric Organ Failure, Beijing, China
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Ai N, Liang Y, Yuan H, Ouyang D, Xie S, Liu X. GDCL-NcDA: identifying non-coding RNA-disease associations via contrastive learning between deep graph learning and deep matrix factorization. BMC Genomics 2023; 24:424. [PMID: 37501127 PMCID: PMC10373414 DOI: 10.1186/s12864-023-09501-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 07/02/2023] [Indexed: 07/29/2023] Open
Abstract
Non-coding RNAs (ncRNAs) draw much attention from studies widely in recent years because they play vital roles in life activities. As a good complement to wet experiment methods, computational prediction methods can greatly save experimental costs. However, high false-negative data and insufficient use of multi-source information can affect the performance of computational prediction methods. Furthermore, many computational methods do not have good robustness and generalization on different datasets. In this work, we propose an effective end-to-end computing framework, called GDCL-NcDA, of deep graph learning and deep matrix factorization (DMF) with contrastive learning, which identifies the latent ncRNA-disease association on diverse multi-source heterogeneous networks (MHNs). The diverse MHNs include different similarity networks and proven associations among ncRNAs (miRNAs, circRNAs, and lncRNAs), genes, and diseases. Firstly, GDCL-NcDA employs deep graph convolutional network and multiple attention mechanisms to adaptively integrate multi-source of MHNs and reconstruct the ncRNA-disease association graph. Then, GDCL-NcDA utilizes DMF to predict the latent disease-associated ncRNAs based on the reconstructed graphs to reduce the impact of the false-negatives from the original associations. Finally, GDCL-NcDA uses contrastive learning (CL) to generate a contrastive loss on the reconstructed graphs and the predicted graphs to improve the generalization and robustness of our GDCL-NcDA framework. The experimental results show that GDCL-NcDA outperforms highly related computational methods. Moreover, case studies demonstrate the effectiveness of GDCL-NcDA in identifying the associations among diversiform ncRNAs and diseases.
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Affiliation(s)
- Ning Ai
- Peng Cheng Laboratory, Shenzhen, 518005, Guangdong, China
- School of Computer Science and Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, China
| | - Yong Liang
- Peng Cheng Laboratory, Shenzhen, 518005, Guangdong, China.
- Pazhou Laboratory (Huangpu), Guangzhou, 510555, Guangdong, China.
| | - Haoliang Yuan
- School of Automation, Guangdong University of Technology, Guangzhou, 510006, Guangdong, China
| | - Dong Ouyang
- Peng Cheng Laboratory, Shenzhen, 518005, Guangdong, China
- School of Computer Science and Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, China
| | - Shengli Xie
- Institute of Intelligent Information Processing, Guangdong University of Technology, Guangzhou, 510000, Guangdong, China
| | - Xiaoying Liu
- Computer Engineering Technical College, Guangdong Polytechnic of Science and Technology, Zhuhai, Guangdong, 519090, China
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Rego N, Libisch MG, Rovira C, Tosar JP, Robello C. Comparative microRNA profiling of Trypanosoma cruzi infected human cells. Front Cell Infect Microbiol 2023; 13:1187375. [PMID: 37424776 PMCID: PMC10322668 DOI: 10.3389/fcimb.2023.1187375] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 06/01/2023] [Indexed: 07/11/2023] Open
Abstract
Introduction Trypanosoma cruzi, the causative agent of Chagas disease, can infect almost any nucleated cell in the mammalian host. Although previous studies have described the transcriptomic changes that occur in host cells during parasite infection, the understanding of the role of post-transcriptional regulation in this process is limited. MicroRNAs, a class of short non-coding RNAs, are key players in regulating gene expression at the post-transcriptional level, and their involvement in the host-T. cruzi interplay is a growing area of research. However, to our knowledge, there are no comparative studies on the microRNA changes that occur in different cell types in response to T. cruzi infection. Methods and results Here we investigated microRNA changes in epithelial cells, cardiomyocytes and macrophages infected with T. cruzi for 24 hours, using small RNA sequencing followed by careful bioinformatics analysis. We show that, although microRNAs are highly cell type-specific, a signature of three microRNAs -miR-146a, miR-708 and miR-1246, emerges as consistently responsive to T. cruzi infection across representative human cell types. T. cruzi lacks canonical microRNA-induced silencing mechanisms and we confirm that it does not produce any small RNA that mimics known host microRNAs. We found that macrophages show a broad response to parasite infection, while microRNA changes in epithelial and cardiomyocytes are modest. Complementary data indicated that cardiomyocyte response may be greater at early time points of infection. Conclusions Our findings emphasize the significance of considering microRNA changes at the cellular level and complement previous studies conducted at higher organizational levels, such as heart samples. While miR-146a has been previously implicated in T. cruzi infection, similarly to its involvement in many other immunological responses, miR-1246 and miR-708 are demonstrated here for the first time. Given their expression in multiple cell types, we anticipate our work as a starting point for future investigations into their role in the post-transcriptional regulation of T. cruzi infected cells and their potential as biomarkers for Chagas disease.
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Affiliation(s)
- Natalia Rego
- Unidad de Bioinformática, Institut Pasteur de Montevideo, Montevideo, Uruguay
- Laboratorio de Genómica Evolutiva, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay
| | - María Gabriela Libisch
- Laboratorio de Interacciones Hospedero Patógeno/UBM, Institut Pasteur de Montevideo, Montevideo, Uruguay
| | - Carlos Rovira
- Department of Clinical Sciences Lund, Division of Oncology, Lund University, Lund, Sweden
| | - Juan Pablo Tosar
- Laboratorio de Genómica Funcional, Institut Pasteur de Montevideo, Montevideo, Uruguay
- Unidad de Bioquímica Analítica, Centro de Investigaciones Nucleares, Facultad de Ciencias, Universidad de la República, Montevideo, Uruguay
| | - Carlos Robello
- Laboratorio de Interacciones Hospedero Patógeno/UBM, Institut Pasteur de Montevideo, Montevideo, Uruguay
- Departamento de Bioquímica, Facultad de Medicina, Universidad de la República, Montevideo, Uruguay
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Li S, Chang M, Tong L, Wang Y, Wang M, Wang F. Screening potential lncRNA biomarkers for breast cancer and colorectal cancer combining random walk and logistic matrix factorization. Front Genet 2023; 13:1023615. [PMID: 36744179 PMCID: PMC9895102 DOI: 10.3389/fgene.2022.1023615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Accepted: 10/10/2022] [Indexed: 01/21/2023] Open
Abstract
Breast cancer and colorectal cancer are two of the most common malignant tumors worldwide. They cause the leading causes of cancer mortality. Many researches have demonstrated that long noncoding RNAs (lncRNAs) have close linkages with the occurrence and development of the two cancers. Therefore, it is essential to design an effective way to identify potential lncRNA biomarkers for them. In this study, we developed a computational method (LDA-RWLMF) by integrating random walk with restart and Logistic Matrix Factorization to investigate the roles of lncRNA biomarkers in the prognosis and diagnosis of the two cancers. We first fuse disease semantic and Gaussian association profile similarities and lncRNA functional and Gaussian association profile similarities. Second, we design a negative selection algorithm to extract negative LncRNA-Disease Associations (LDA) based on random walk. Third, we develop a logistic matrix factorization model to predict possible LDAs. We compare our proposed LDA-RWLMF method with four classical LDA prediction methods, that is, LNCSIM1, LNCSIM2, ILNCSIM, and IDSSIM. The results from 5-fold cross validation on the MNDR dataset show that LDA-RWLMF computes the best AUC value of 0.9312, outperforming the above four LDA prediction methods. Finally, we rank all lncRNA biomarkers for the two cancers after determining the performance of LDA-RWLMF, respectively. We find that 48 and 50 lncRNAs have the highest association scores with breast cancer and colorectal cancer among all lncRNAs known to associate with them on the MNDR dataset, respectively. We predict that lncRNAs HULC and HAR1A could be separately potential biomarkers for breast cancer and colorectal cancer and need to biomedical experimental validation.
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Wang B, Liu R, Zheng X, Du X, Wang Z. lncRNA-disease association prediction based on matrix decomposition of elastic network and collaborative filtering. Sci Rep 2022; 12:12700. [PMID: 35882886 PMCID: PMC9325687 DOI: 10.1038/s41598-022-16594-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2022] [Accepted: 07/12/2022] [Indexed: 11/30/2022] Open
Abstract
In recent years, with the continuous development and innovation of high-throughput biotechnology, more and more evidence show that lncRNA plays an essential role in biological life activities and is related to the occurrence of various diseases. However, due to the high cost and time-consuming of traditional biological experiments, the number of associations between lncRNAs and diseases that rely on experiments to verify is minimal. Computer-aided study of lncRNA-disease association is an important method to study the development of the lncRNA-disease association. Using the existing data to establish a prediction model and predict the unknown lncRNA-disease association can make the biological experiment targeted and improve its accuracy of the biological experiment. Therefore, we need to find an accurate and efficient method to predict the relationship between lncRNA and diseases and help biologists complete the diagnosis and treatment of diseases. Most of the current lncRNA-disease association predictions do not consider the model instability caused by the actual data. Also, predictive models may produce data that overfit is not considered. This paper proposes a lncRNA-disease association prediction model (ENCFLDA) that combines an elastic network with matrix decomposition and collaborative filtering. This method uses the existing lncRNA-miRNA association data and miRNA-disease association data to predict the association between unknown lncRNA and disease, updates the matrix by matrix decomposition combined with the elastic network, and then obtains the final prediction matrix by collaborative filtering. This method uses the existing lncRNA-miRNA association data and miRNA-disease association data to predict the association of unknown lncRNAs with diseases. First, since the known lncRNA-disease association matrix is very sparse, the cosine similarity and KNN are used to update the lncRNA-disease association matrix. The matrix is then updated by matrix decomposition combined with an elastic net algorithm, to increase the stability of the overall prediction model and eliminate data overfitting. The final prediction matrix is then obtained through collaborative filtering based on lncRNA.Through simulation experiments, the results show that the AUC value of ENCFLDA can reach 0.9148 under the framework of LOOCV, which is higher than the prediction result of the latest model.
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Affiliation(s)
- Bo Wang
- College of Computer and Control, Qiqihar University, Qiqihar, 161006, China.
| | - RunJie Liu
- College of Computer and Control, Qiqihar University, Qiqihar, 161006, China
| | - XiaoDong Zheng
- College of Computer and Control, Qiqihar University, Qiqihar, 161006, China
| | - XiaoXin Du
- College of Computer and Control, Qiqihar University, Qiqihar, 161006, China
| | - ZhengFei Wang
- College of Computer and Control, Qiqihar University, Qiqihar, 161006, China
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Guo Z, Hui Y, Kong F, Lin X. Finding Lung-Cancer-Related lncRNAs Based on Laplacian Regularized Least Squares With Unbalanced Bi-Random Walk. Front Genet 2022; 13:933009. [PMID: 35938010 PMCID: PMC9355720 DOI: 10.3389/fgene.2022.933009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 06/03/2022] [Indexed: 11/13/2022] Open
Abstract
Lung cancer is one of the leading causes of cancer-related deaths. Thus, it is important to find its biomarkers. Furthermore, there is an increasing number of studies reporting that long noncoding RNAs (lncRNAs) demonstrate dense linkages with multiple human complex diseases. Inferring new lncRNA-disease associations help to identify potential biomarkers for lung cancer and further understand its pathogenesis, design new drugs, and formulate individualized therapeutic options for lung cancer patients. This study developed a computational method (LDA-RLSURW) by integrating Laplacian regularized least squares and unbalanced bi-random walk to discover possible lncRNA biomarkers for lung cancer. First, the lncRNA and disease similarities were computed. Second, unbalanced bi-random walk was, respectively, applied to the lncRNA and disease networks to score associations between diseases and lncRNAs. Third, Laplacian regularized least squares were further used to compute the association probability between each lncRNA-disease pair based on the computed random walk scores. LDA-RLSURW was compared using 10 classical LDA prediction methods, and the best AUC value of 0.9027 on the lncRNADisease database was obtained. We found the top 30 lncRNAs associated with lung cancers and inferred that lncRNAs TUG1, PTENP1, and UCA1 may be biomarkers of lung neoplasms, non-small–cell lung cancer, and LUAD, respectively.
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Blood-derived lncRNAs as biomarkers for cancer diagnosis: the Good, the Bad and the Beauty. NPJ Precis Oncol 2022; 6:40. [PMID: 35729321 PMCID: PMC9213432 DOI: 10.1038/s41698-022-00283-7] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 05/13/2022] [Indexed: 11/24/2022] Open
Abstract
Cancer ranks as one of the deadliest diseases worldwide. The high mortality rate associated with cancer is partially due to the lack of reliable early detection methods and/or inaccurate diagnostic tools such as certain protein biomarkers. Cell-free nucleic acids (cfNA) such as circulating long noncoding RNAs (lncRNAs) have been proposed as a new class of potential biomarkers for cancer diagnosis. The reported correlation between the presence of tumors and abnormal levels of lncRNAs in the blood of cancer patients has notably triggered a worldwide interest among clinicians and oncologists who have been actively investigating their potentials as reliable cancer biomarkers. In this report, we review the progress achieved (“the Good”) and challenges encountered (“the Bad”) in the development of circulating lncRNAs as potential biomarkers for early cancer diagnosis. We report and discuss the diagnostic performance of more than 50 different circulating lncRNAs and emphasize their numerous potential clinical applications (“the Beauty”) including therapeutic targets and agents, on top of diagnostic and prognostic capabilities. This review also summarizes the best methods of investigation and provides useful guidelines for clinicians and scientists who desire conducting their own clinical studies on circulating lncRNAs in cancer patients via RT-qPCR or Next Generation Sequencing (NGS).
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Exploring the crosstalk between long non-coding RNAs and microRNAs to unravel potential prognostic and therapeutic biomarkers in β-thalassemia. Mol Biol Rep 2022; 49:7057-7068. [PMID: 35717472 DOI: 10.1007/s11033-022-07629-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 05/19/2022] [Indexed: 10/18/2022]
Abstract
β-thalassemia is a prevalent monogenic disorder characterized by reduced or absent synthesis of the β-globin chain. Although great effort has been made to ameliorate the disease severity of β-thalassemic patients, progress has been stymied due to limited understanding of the detailed molecular mechanism of disease pathogenesis. Recently, non-coding RNAs have been established as key players in regulating various physiological and pathological processes. Many ncRNAs are involved in hematopoiesis and erythroid development. Furthermore, various studies have also reported the complex interplay between different ncRNAs, such as miRNA, lncRNAs, etc. in regulating disease progression and pathogenesis. Both lncRNAs and miRNAs have been identified as independent regulators of globin gene expression and are intricately involved in disease pathogenesis; yet accumulating evidence suggests that the cross-talk between lncRNAs and miRNAs is intricately involved in the underlying globin gene expression, fine-tuning the effect of their independent regulation. In this review, we summarize the current progress of research on the roles of lncRNAs and miRNAs implicated in β-thalassemia disease, including their interactions and regulatory networks. This can provide important insights into the detailed epigenetic regulation of globin gene switching and has the potential to develop novel therapeutic approaches against β-thalassemia.
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Liu Y, Yang H, Zheng C, Wang K, Yan J, Cao H, Zhang Y. NCP-BiRW: A Hybrid Approach for Predicting Long Noncoding RNA-Disease Associations by Network Consistency Projection and Bi-Random Walk. Front Genet 2022; 13:862272. [PMID: 35495166 PMCID: PMC9043107 DOI: 10.3389/fgene.2022.862272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 03/21/2022] [Indexed: 12/06/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) play significant roles in the disease process. Understanding the pathological mechanisms of lncRNAs during the course of various diseases will help clinicians prevent and treat diseases. With the emergence of high-throughput techniques, many biological experiments have been developed to study lncRNA-disease associations. Because experimental methods are costly, slow, and laborious, a growing number of computational models have emerged. Here, we present a new approach using network consistency projection and bi-random walk (NCP-BiRW) to infer hidden lncRNA-disease associations. First, integrated similarity networks for lncRNAs and diseases were constructed by merging similarity information. Subsequently, network consistency projection was applied to calculate space projection scores for lncRNAs and diseases, which were then introduced into a bi-random walk method for association prediction. To test model performance, we employed 5- and 10-fold cross-validation, with the area under the receiver operating characteristic curve as the evaluation indicator. The computational results showed that our method outperformed the other five advanced algorithms. In addition, the novel method was applied to another dataset in the Mammalian ncRNA-Disease Repository (MNDR) database and showed excellent performance. Finally, case studies were carried out on atherosclerosis and leukemia to confirm the effectiveness of our method in practice. In conclusion, we could infer lncRNA-disease associations using the NCP-BiRW model, which may benefit biomedical studies in the future.
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Affiliation(s)
- Yanling Liu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
- Department of Mathematics, Changzhi Medical College, Changzhi, China
| | - Hong Yang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Chu Zheng
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Ke Wang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Jingjing Yan
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Hongyan Cao
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Yanbo Zhang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
- Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China
- School of Health and Service Management, Shanxi University of Chinese Medicine, Taiyuan, China
- *Correspondence:Yanbo Zhang,
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Nguyen VT, Le TTK, Nguyen TQV, Tran DH. Inferring miRNA-disease associations using collaborative filtering and resource allocation on a tripartite graph. BMC Med Genomics 2021; 14:225. [PMID: 34789252 PMCID: PMC8600685 DOI: 10.1186/s12920-021-01078-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 09/07/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Developing efficient and successful computational methods to infer potential miRNA-disease associations is urgently needed and is attracting many computer scientists in recent years. The reason is that miRNAs are involved in many important biological processes and it is tremendously expensive and time-consuming to do biological experiments to verify miRNA-disease associations. METHODS In this paper, we proposed a new method to infer miRNA-disease associations using collaborative filtering and resource allocation algorithms on a miRNA-disease-lncRNA tripartite graph. It combined the collaborative filtering algorithm in CFNBC model to solve the problem of imbalanced data and the method for association prediction established multiple types of known associations among multiple objects presented in TPGLDA model. RESULTS The experimental results showed that our proposed method achieved a reliable performance with Area Under Roc Curve (AUC) and Area Under Precision-Recall Curve (AUPR) values of 0.9788 and 0.9373, respectively, under fivefold-cross-validation experiments. It outperformed than some other previous methods such as DCSMDA and TPGLDA. Furthermore, it demonstrated the ability to derive new associations between miRNAs and diseases among 8, 19 and 14 new associations out of top 40 predicted associations in case studies of Prostatic Neoplasms, Heart Failure, and Glioma diseases, respectively. All of these new predicted associations have been confirmed by recent literatures. Besides, it could discover new associations for new diseases (or miRNAs) without any known associations as demonstrated in the case study of Open-angle glaucoma disease. CONCLUSION With the reliable performance to infer new associations between miRNAs and diseases as well as to discover new associations for new diseases (or miRNAs) without any known associations, our proposed method can be considered as a powerful tool to infer miRNA-disease associations.
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Affiliation(s)
- Van Tinh Nguyen
- Faculty of Information Technology, Hanoi University of Industry, Hanoi, Vietnam
- Faculty of Information Technology, Hanoi National University of Education, Hanoi, Vietnam
| | - Thi Tu Kien Le
- Faculty of Information Technology, Hanoi National University of Education, Hanoi, Vietnam
| | - Tran Quoc Vinh Nguyen
- Faculty of Information Technology, The University of Da Nang - University of Science and Education, Da Nang, Vietnam
| | - Dang Hung Tran
- Faculty of Information Technology, Hanoi National University of Education, Hanoi, Vietnam.
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14
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Multi-Run Concrete Autoencoder to Identify Prognostic lncRNAs for 12 Cancers. Int J Mol Sci 2021; 22:ijms222111919. [PMID: 34769351 PMCID: PMC8584911 DOI: 10.3390/ijms222111919] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 10/28/2021] [Accepted: 10/30/2021] [Indexed: 12/13/2022] Open
Abstract
Background: Long non-coding RNA plays a vital role in changing the expression profiles of various target genes that lead to cancer development. Thus, identifying prognostic lncRNAs related to different cancers might help in developing cancer therapy. Method: To discover the critical lncRNAs that can identify the origin of different cancers, we propose the use of the state-of-the-art deep learning algorithm concrete autoencoder (CAE) in an unsupervised setting, which efficiently identifies a subset of the most informative features. However, CAE does not identify reproducible features in different runs due to its stochastic nature. We thus propose a multi-run CAE (mrCAE) to identify a stable set of features to address this issue. The assumption is that a feature appearing in multiple runs carries more meaningful information about the data under consideration. The genome-wide lncRNA expression profiles of 12 different types of cancers, with a total of 4768 samples available in The Cancer Genome Atlas (TCGA), were analyzed to discover the key lncRNAs. The lncRNAs identified by multiple runs of CAE were added to a final list of key lncRNAs that are capable of identifying 12 different cancers. Results: Our results showed that mrCAE performs better in feature selection than single-run CAE, standard autoencoder (AE), and other state-of-the-art feature selection techniques. This study revealed a set of top-ranking 128 lncRNAs that could identify the origin of 12 different cancers with an accuracy of 95%. Survival analysis showed that 76 of 128 lncRNAs have the prognostic capability to differentiate high- and low-risk groups of patients with different cancers. Conclusion: The proposed mrCAE, which selects actual features, outperformed the AE even though it selects the latent or pseudo-features. By selecting actual features instead of pseudo-features, mrCAE can be valuable for precision medicine. The identified prognostic lncRNAs can be further studied to develop therapies for different cancers.
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15
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Wang B, Zhang C, Du XX, Zhang JF. lncRNA-disease association prediction based on latent factor model and projection. Sci Rep 2021; 11:19965. [PMID: 34620945 PMCID: PMC8497550 DOI: 10.1038/s41598-021-99493-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 09/27/2021] [Indexed: 02/08/2023] Open
Abstract
Computer aided research of lncRNA-disease association is an important way to study the development of lncRNA-disease. The correlation analysis of existing data, the establishment of prediction model, prediction of unknown lncRNA-disease association, can make the biological experiment targeted, improve the accuracy of biological experiment. In this paper, a lncRNA-disease association prediction model based on latent factor model and projection is proposed (LFMP). This method uses lncRNA-miRNA association data and miRNA-disease association data to predict the unknown lncRNA-disease association, so this method does not need lncRNA-disease association data. The simulation results show that under the LOOCV framework, the AUC of LFMP can reach 0.8964. Better than the latest results. Through the case study of lung and colorectal tumors, LFMP can effectively infer the undetected lncRNA-disease association.
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Affiliation(s)
- Bo Wang
- grid.412616.60000 0001 0002 2355College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161006 People’s Republic of China
| | - Chao Zhang
- grid.412616.60000 0001 0002 2355College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161006 People’s Republic of China
| | - Xiao-xin Du
- grid.412616.60000 0001 0002 2355College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161006 People’s Republic of China
| | - Jian-fei Zhang
- grid.412616.60000 0001 0002 2355College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161006 People’s Republic of China
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16
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Identification of genes, pathways and transcription factor-miRNA-target gene networks and experimental verification in venous thromboembolism. Sci Rep 2021; 11:16352. [PMID: 34381164 PMCID: PMC8357955 DOI: 10.1038/s41598-021-95909-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2020] [Accepted: 08/02/2021] [Indexed: 12/17/2022] Open
Abstract
Venous thromboembolism (VTE) is a complex, multifactorial life-threatening disease that involves vascular endothelial cell (VEC) dysfunction. However, the exact pathogenesis and underlying mechanisms of VTE are not completely clear. The aim of this study was to identify the core genes and pathways in VECs that are involved in the development and progression of unprovoked VTE (uVTE). The microarray dataset GSE118259 was downloaded from the Gene Expression Omnibus database, and 341 up-regulated and 8 down-regulated genes were identified in the VTE patients relative to the healthy controls, including CREB1, HIF1α, CBL, ILK, ESM1 and the ribosomal protein family genes. The protein–protein interaction (PPI) network and the transcription factor (TF)-miRNA-target gene network were constructed with these differentially expressed genes (DEGs), and visualized using Cytoscape software 3.6.1. Eighty-nine miRNAs were predicted as the targeting miRNAs of the DEGs, and 197 TFs were predicted as regulators of these miRNAs. In addition, 237 node genes and 4 modules were identified in the PPI network. The significantly enriched pathways included metabolic, cell adhesion, cell proliferation and cellular response to growth factor stimulus pathways. CREB1 was a differentially expressed TF in the TF-miRNA-target gene network, which regulated six miRNA-target gene pairs. The up-regulation of ESM1, HIF1α and CREB1 was confirmed at the mRNA and protein level in the plasma of uVTE patients. Taken together, ESM1, HIF1α and the CREB1-miRNA-target genes axis play potential mechanistic roles in uVTE development.
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17
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Chowdhary A, Satagopam V, Schneider R. Long Non-coding RNAs: Mechanisms, Experimental, and Computational Approaches in Identification, Characterization, and Their Biomarker Potential in Cancer. Front Genet 2021; 12:649619. [PMID: 34276764 PMCID: PMC8281131 DOI: 10.3389/fgene.2021.649619] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 04/20/2021] [Indexed: 01/09/2023] Open
Abstract
Long non-coding RNAs are diverse class of non-coding RNA molecules >200 base pairs of length having various functions like gene regulation, dosage compensation, epigenetic regulation. Dysregulation and genomic variations of several lncRNAs have been implicated in several diseases. Their tissue and developmental specific expression are contributing factors for them to be viable indicators of physiological states of the cells. Here we present an comprehensive review the molecular mechanisms and functions, state of the art experimental and computational pipelines and challenges involved in the identification and functional annotation of lncRNAs and their prospects as biomarkers. We also illustrate the application of co-expression networks on the TCGA-LIHC dataset for putative functional predictions of lncRNAs having a therapeutic potential in Hepatocellular carcinoma (HCC).
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Affiliation(s)
- Anshika Chowdhary
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Venkata Satagopam
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
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18
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Yan H, Chai H, Zhao H. Detecting lncRNA-Cancer Associations by Combining miRNAs, Genes, and Prognosis With Matrix Factorization. Front Genet 2021; 12:639872. [PMID: 34262591 PMCID: PMC8273282 DOI: 10.3389/fgene.2021.639872] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Accepted: 04/15/2021] [Indexed: 11/13/2022] Open
Abstract
Motivation: Long non-coding RNAs (lncRNAs) play important roles in cancer development. Prediction of lncRNA–cancer association is necessary for efficiently discovering biomarkers and designing treatment for cancers. Currently, several methods have been developed to predict lncRNA–cancer associations. However, most of them do not consider the relationships between lncRNA with other molecules and with cancer prognosis, which has limited the accuracy of the prediction. Method: Here, we constructed relationship matrices between 1,679 lncRNAs, 2,759 miRNAs, and 16,410 genes and cancer prognosis on three types of cancers (breast, lung, and colorectal cancers) to predict lncRNA–cancer associations. The matrices were iteratively reconstructed by matrix factorization to optimize low-rank size. This method is called detecting lncRNA cancer association (DRACA). Results: Application of this method in the prediction of lncRNAs–breast cancer, lncRNA–lung cancer, and lncRNA–colorectal cancer associations achieved an area under curve (AUC) of 0.810, 0.796, and 0.795, respectively, by 10-fold cross-validations. The performances of DRACA in predicting associations between lncRNAs with three kinds of cancers were at least 6.6, 7.2, and 6.9% better than other methods, respectively. To our knowledge, this is the first method employing cancer prognosis in the prediction of lncRNA–cancer associations. When removing the relationships between cancer prognosis and genes, the AUCs were decreased 7.2, 0.6, and 5% for breast, lung, and colorectal cancers, respectively. Moreover, the predicted lncRNAs were found with greater numbers of somatic mutations than the lncRNAs not predicted as cancer-associated for three types of cancers. DRACA predicted many novel lncRNAs, whose expressions were found to be related to survival rates of patients. The method is available at https://github.com/Yanh35/DRACA.
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Affiliation(s)
- Huan Yan
- Department of Medical Research Center, Sun Yat-sen Memorial Hospital, Guangzhou, China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China
| | - Hua Chai
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
| | - Huiying Zhao
- Department of Medical Research Center, Sun Yat-sen Memorial Hospital, Guangzhou, China.,Guangdong Provincial Key Laboratory of Malignant Tumor Epigenetics and Gene Regulation, Guangzhou, China
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19
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Yuan L, Zhao J, Sun T, Shen Z. A machine learning framework that integrates multi-omics data predicts cancer-related LncRNAs. BMC Bioinformatics 2021; 22:332. [PMID: 34134612 PMCID: PMC8210375 DOI: 10.1186/s12859-021-04256-8] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 06/07/2021] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND LncRNAs (Long non-coding RNAs) are a type of non-coding RNA molecule with transcript length longer than 200 nucleotides. LncRNA has been novel candidate biomarkers in cancer diagnosis and prognosis. However, it is difficult to discover the true association mechanism between lncRNAs and complex diseases. The unprecedented enrichment of multi-omics data and the rapid development of machine learning technology provide us with the opportunity to design a machine learning framework to study the relationship between lncRNAs and complex diseases. RESULTS In this article, we proposed a new machine learning approach, namely LGDLDA (LncRNA-Gene-Disease association networks based LncRNA-Disease Association prediction), for disease-related lncRNAs association prediction based multi-omics data, machine learning methods and neural network neighborhood information aggregation. Firstly, LGDLDA calculates the similarity matrix of lncRNA, gene and disease respectively, and it calculates the similarity between lncRNAs through the lncRNA expression profile matrix, lncRNA-miRNA interaction matrix and lncRNA-protein interaction matrix. We obtain gene similarity matrix by calculating the lncRNA-gene association matrix and the gene-disease association matrix, and we obtain disease similarity matrix by calculating the disease ontology, the disease-miRNA association matrix, and Gaussian interaction profile kernel similarity. Secondly, LGDLDA integrates the neighborhood information in similarity matrices by using nonlinear feature learning of neural network. Thirdly, LGDLDA uses embedded node representations to approximate the observed matrices. Finally, LGDLDA ranks candidate lncRNA-disease pairs and then selects potential disease-related lncRNAs. CONCLUSIONS Compared with lncRNA-disease prediction methods, our proposed method takes into account more critical information and obtains the performance improvement cancer-related lncRNA predictions. Randomly split data experiment results show that the stability of LGDLDA is better than IDHI-MIRW, NCPLDA, LncDisAP and NCPHLDA. The results on different simulation data sets show that LGDLDA can accurately and effectively predict the disease-related lncRNAs. Furthermore, we applied the method to three real cancer data including gastric cancer, colorectal cancer and breast cancer to predict potential cancer-related lncRNAs.
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Affiliation(s)
- Lin Yuan
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Daxue Road 3501, Jinan, 250353, Shandong, China
| | - Jing Zhao
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Daxue Road 3501, Jinan, 250353, Shandong, China
| | - Tao Sun
- School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Daxue Road 3501, Jinan, 250353, Shandong, China
| | - Zhen Shen
- School of Computer and Software, Nanyang Institute of Technology, Changjiang Road 80, Nanyang, 473004, Henan, China.
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20
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Yang Z, Xu F, Wang H, Teschendorff AE, Xie F, He Y. Pan-cancer characterization of long non-coding RNA and DNA methylation mediated transcriptional dysregulation. EBioMedicine 2021; 68:103399. [PMID: 34044218 PMCID: PMC8245911 DOI: 10.1016/j.ebiom.2021.103399] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 04/29/2021] [Accepted: 04/29/2021] [Indexed: 01/24/2023] Open
Abstract
BACKGROUND Disruption of DNA methylation (DNAm) is one of the key signatures of cancer, however, detailed mechanisms that alter the DNA methylome in cancer remain to be elucidated. METHODS Here we present a novel integrative analysis framework, called MeLncTRN (Methylation mediated LncRNA Transcriptional Regulatory Network), that integrates genome-wide transcriptome, DNA methylome and copy number variation profiles, to systematically identify the epigenetically-driven lncRNA-gene regulation circuits across 18 cancer types. FINDING We show that a significant fraction of the aberrant DNAm and gene expression landscape in cancer is associated with long noncoding RNAs (lncRNAs). We reveal distinct types of regulation between lncRNA modulators and target genes that are operative in either only specific cancers or across cancers. Functional studies identified a common theme of cancer hallmarks that lncRNA modulators may participate in. The coupled lncRNA gene interactions via DNAm also serve as markers for classifications of cancer subtypes with different prognoses. INTERPRETATION Our study reveals a vital layer of DNAm and associated expression regulation for many cancer-related genes and we also provide a valuable database resource for interrogating epigenetically mediated lncRNA-gene interactions in cancer. FUNDING National Natural Science Foundation of China [91959106, 31871255].
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Affiliation(s)
- Zhen Yang
- Center for Medical Research and Innovation of Pudong Hospital, Fudan University, and the Shanghai Key Laboratory of Medical Epigenetics, the International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China.
| | - Feng Xu
- Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Haizhou Wang
- Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Andrew E Teschendorff
- CAS Key Lab of Computational Biology, Shanghai Institute for Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China
| | - Feng Xie
- Soochow University, 8 Jixue Road, Suzhou 215131, Jiangsu Province, China
| | - Yungang He
- Shanghai Key Laboratory of Medical Epigenetics, International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China.
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21
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Tian L, Wang SL. Exploring miRNA Sponge Networks of Breast Cancer by Combining miRNA-disease-lncRNA and miRNA-target Networks. Curr Bioinform 2021. [DOI: 10.2174/1574893615999200711171530] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Background:
Recently, ample researches show that microRNAs (miRNAs) not only
interact with coding genes but interact with a pool of different RNAs. Those RNAs are called
miRNA sponges, including long non-coding RNAs (lncRNAs), circular RNA, pseudogenes and
various messenger RNAs. Understanding regulatory networks of miRNA sponges can better help
researchers to study the mechanisms of breast cancers.
Objective:
We develop a new method to explore miRNA sponge networks of breast cancer by combining miRNAdisease-lncRNA and miRNA-target networks (MSNMDL).
Method:
Firstly, MSNMDL infers miRNA-lncRNA functional similarity networks from miRNAdisease-
lncRNA networks. Secondly, MSNMDL forms lncRNA-target networks by using lncRNA
to replace the role of matched miRNA in miRNA-target networks according to the lncRNA-miRNA
pair of miRNA-lncRNA functional similarity networks. And MSNMDL only retains the genes of
breast cancer in lncRNA-target networks to construct candidate miRNA sponge networks. Thirdly,
MSNMDL merges these candidate miRNA sponge networks with other miRNA sponge interactions
and then selects top-hub lncRNA and its interactions to construct miRNA sponge networks.
Results:
MSNMDL is superior to other methods in terms of biological significance and its identified modules might
act as module signatures for prognostication of breast cancer.
Conclusion:
MiRNA sponge networks identified by MSNMDL are biologically significant and are
closely associated with breast cancer, which makes MSNMDL a promising way for researchers to
study the pathogenesis of breast cancer.
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Affiliation(s)
- Lei Tian
- School of Information Science and Engineering, Hunan University, Changsha, China
| | - Shu-Lin Wang
- School of Information Science and Engineering, Hunan University, Changsha, China
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22
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Hu J, Li W, Huang B, Zhao Q, Fan X. The Profiles of Long Non-coding RNA and mRNA Transcriptome Reveals the Genes and Pathway Potentially Involved in Pasteurella multocida Infection of New Zealand Rabbits. Front Vet Sci 2021; 8:591273. [PMID: 34026883 PMCID: PMC8131872 DOI: 10.3389/fvets.2021.591273] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 03/19/2021] [Indexed: 12/22/2022] Open
Abstract
Infection with Pasteurella multocida (P. multocida) causes severe epidemic diseases in rabbits and is responsible for the pronounced economic losses in the livestock industry. Long non-coding RNAs (lncRNAs) have been proven to exert vital functions in regulating the host immune responses to bacterial attacks. However, little is known about how lncRNAs participate in the rabbit's immune response against P. multocida infection in the lungs. LncRNA and mRNA expression profiles were analyzed by transcriptomics and bioinformatics during P. multocida infection. A total of 336 lncRNAs and 7,014 mRNAs were differentially regulated at 1 day and 3 days post infection (dpi). Nearly 80% of the differentially expressed lncRNAs exhibited an increased expression at 3 dpi suggesting that the P. multocida genes are responsible for regulation. Moreover, GO and KEGG enriched analysis indicated that the immune-related pathways including pattern recognition receptors (PRRs), cytokines, and chemokines were significantly enriched at 3 dpi. These results indicate that the dysregulated immune-related genes may play crucial roles in defending against P. multocida attacks. Overall, these results advance our cognition of the role of lncRNAs and mRNAs in modulating the rabbit's innate immune response against P. multocida attacks, which will offer a valuable clue for further studies into exploring P. multocida-related diseases in human.
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Affiliation(s)
- Jiaqing Hu
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Taian, China
| | - Wenqiang Li
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Taian, China
| | - Bing Huang
- Shandong Provincial Key Laboratory of Poultry Disease Diagnose and Immune, Institute of Poultry, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Qiaoya Zhao
- Shandong Provincial Key Laboratory of Poultry Disease Diagnose and Immune, Institute of Poultry, Shandong Academy of Agricultural Sciences, Jinan, China
| | - Xinzhong Fan
- Shandong Provincial Key Laboratory of Animal Biotechnology and Disease Control and Prevention, College of Animal Science and Veterinary Medicine, Shandong Agricultural University, Taian, China
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Computational Methods and Online Resources for Identification of piRNA-Related Molecules. Interdiscip Sci 2021; 13:176-191. [PMID: 33886096 DOI: 10.1007/s12539-021-00428-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 03/26/2021] [Accepted: 03/29/2021] [Indexed: 02/07/2023]
Abstract
piRNAs are a class of small non-coding RNA molecules, which interact with the PIWI family and have many important and diverse biological functions. The present review is aimed to provide guidelines and contribute to piRNA research. We focused on the four types of identification models on piRNA-related molecules, including piRNA, piRNA cluster, piRNA target, and disease-related piRNA. We evaluated the types of tools for the identification of piRNAs based on five aspects: datasets, features, classifiers, performance, and usability. We found the precision of 2lpiRNApred was the highest in datasets of model organisms, piRNN had a better performance of datasets of non-model organisms, and 2L-piRNA had the fastest recognition speed of all tools. In addition, we presented an overview of piRNA databases. The databases were divided into six categories: basic annotation, comprehensive annotation, isoform, cluster, target, and disease. We found that piRNA data of non-model organisms, piRNA target data, and piRNA-disease-associated data should be strengthened. Our review might assist researchers in selecting appropriate tools or datasets for their studies, reveal potential problems and shed light on future bioinformatics studies.
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24
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MicroRNAs and long non-coding RNAs as novel regulators of ribosome biogenesis. Biochem Soc Trans 2021; 48:595-612. [PMID: 32267487 PMCID: PMC7200637 DOI: 10.1042/bst20190854] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 03/12/2020] [Accepted: 03/16/2020] [Indexed: 12/14/2022]
Abstract
Ribosome biogenesis is the fine-tuned, essential process that generates mature ribosomal subunits and ultimately enables all protein synthesis within a cell. Novel regulators of ribosome biogenesis continue to be discovered in higher eukaryotes. While many known regulatory factors are proteins or small nucleolar ribonucleoproteins, microRNAs (miRNAs), and long non-coding RNAs (lncRNAs) are emerging as a novel modulatory layer controlling ribosome production. Here, we summarize work uncovering non-coding RNAs (ncRNAs) as novel regulators of ribosome biogenesis and highlight their links to diseases of defective ribosome biogenesis. It is still unclear how many miRNAs or lncRNAs are involved in phenotypic or pathological disease outcomes caused by impaired ribosome production, as in the ribosomopathies, or by increased ribosome production, as in cancer. In time, we hypothesize that many more ncRNA regulators of ribosome biogenesis will be discovered, which will be followed by an effort to establish connections between disease pathologies and the molecular mechanisms of this additional layer of ribosome biogenesis control.
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25
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Xie G, Huang B, Sun Y, Wu C, Han Y. RWSF-BLP: a novel lncRNA-disease association prediction model using random walk-based multi-similarity fusion and bidirectional label propagation. Mol Genet Genomics 2021; 296:473-483. [PMID: 33590345 DOI: 10.1007/s00438-021-01764-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Accepted: 01/28/2021] [Indexed: 12/13/2022]
Abstract
An increasing number of studies and experiments have demonstrated that long noncoding RNAs (lncRNAs) have a massive impact on various biological processes. Predicting potential associations between lncRNAs and diseases not only can improve our understanding of the molecular mechanisms of human diseases but also can facilitate the identification of biomarkers for disease diagnosis, treatment, and prevention. However, identifying such associations through experiments is costly and demanding, thereby prompting researchers to develop computational methods to complement these experiments. In this paper, we constructed a novel model called RWSF-BLP (a novel lncRNA-disease association prediction model using Random Walk-based multi-Similarity Fusion and Bidirectional Label Propagation), which applies an efficient random walk-based multi-similarity fusion (RWSF) method to fuse different similarity matrices and utilizes bidirectional label propagation to predict potential lncRNA-disease associations. Leave-one-out cross-validation (LOOCV) and 5-fold cross-validation (5-fold-CV) were implemented in the evaluation RWSF-BLP performance. Results showed that, RWSF-BLP has reliable AUCs of 0.9086 and 0.9115 ± 0.0044 under the framework of LOOCV and 5-fold-CV and outperformed other four canonical methods. Case studies on lung cancer and leukemia demonstrated that potential lncRNA-disease associations can be predicted through our method. Therefore, our method can accurately infer potential lncRNA-disease associations and may be a good choice in future biomedical research.
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Affiliation(s)
- Guobo Xie
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Bin Huang
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Yuping Sun
- School of Computer Science, Guangdong University of Technology, Guangzhou, China.
| | - Changhai Wu
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
| | - Yuqiong Han
- School of Computer Science, Guangdong University of Technology, Guangzhou, China
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26
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Chen J, Zhang J, Gao Y, Li Y, Feng C, Song C, Ning Z, Zhou X, Zhao J, Feng M, Zhang Y, Wei L, Pan Q, Jiang Y, Qian F, Han J, Yang Y, Wang Q, Li C. LncSEA: a platform for long non-coding RNA related sets and enrichment analysis. Nucleic Acids Res 2021; 49:D969-D980. [PMID: 33045741 PMCID: PMC7778898 DOI: 10.1093/nar/gkaa806] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 09/03/2020] [Accepted: 09/30/2020] [Indexed: 02/01/2023] Open
Abstract
Long non-coding RNAs (lncRNAs) have been proven to play important roles in transcriptional processes and various biological functions. Establishing a comprehensive collection of human lncRNA sets is urgent work at present. Using reference lncRNA sets, enrichment analyses will be useful for analyzing lncRNA lists of interest submitted by users. Therefore, we developed a human lncRNA sets database, called LncSEA, which aimed to document a large number of available resources for human lncRNA sets and provide annotation and enrichment analyses for lncRNAs. LncSEA supports >40 000 lncRNA reference sets across 18 categories and 66 sub-categories, and covers over 50 000 lncRNAs. We not only collected lncRNA sets based on downstream regulatory data sources, but also identified a large number of lncRNA sets regulated by upstream transcription factors (TFs) and DNA regulatory elements by integrating TF ChIP-seq, DNase-seq, ATAC-seq and H3K27ac ChIP-seq data. Importantly, LncSEA provides annotation and enrichment analyses of lncRNA sets associated with upstream regulators and downstream targets. In summary, LncSEA is a powerful platform that provides a variety of types of lncRNA sets for users, and supports lncRNA annotations and enrichment analyses. The LncSEA database is freely accessible at http://bio.liclab.net/LncSEA/index.php.
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Affiliation(s)
- Jiaxin Chen
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Jian Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yu Gao
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yanyu Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Chenchen Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Chao Song
- Department of Pharmacology, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Ziyu Ning
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Xinyuan Zhou
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Jianmei Zhao
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Minghong Feng
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yuexin Zhang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Ling Wei
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Qi Pan
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Yong Jiang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Fengcui Qian
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Junwei Han
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Yongsan Yang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Qiuyu Wang
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
| | - Chunquan Li
- School of Medical Informatics, Daqing Campus, Harbin Medical University, Daqing 163319, China
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27
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Zhao L, Wang J, Li Y, Song T, Wu Y, Fang S, Bu D, Li H, Sun L, Pei D, Zheng Y, Huang J, Xu M, Chen R, Zhao Y, He S. NONCODEV6: an updated database dedicated to long non-coding RNA annotation in both animals and plants. Nucleic Acids Res 2021; 49:D165-D171. [PMID: 33196801 PMCID: PMC7779048 DOI: 10.1093/nar/gkaa1046] [Citation(s) in RCA: 137] [Impact Index Per Article: 45.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 10/13/2020] [Accepted: 10/22/2020] [Indexed: 02/06/2023] Open
Abstract
NONCODE (http://www.noncode.org/) is a comprehensive database of collection and annotation of noncoding RNAs, especially long non-coding RNAs (lncRNAs) in animals. NONCODEV6 is dedicated to providing the full scope of lncRNAs across plants and animals. The number of lncRNAs in NONCODEV6 has increased from 548 640 to 644 510 since the last update in 2017. The number of human lncRNAs has increased from 172 216 to 173 112. The number of mouse lncRNAs increased from 131 697 to 131 974. The number of plant lncRNAs is 94 697. The relationship between lncRNAs in human and cancer were updated with transcriptome sequencing profiles. Three important new features were also introduced in NONCODEV6: (i) updated human lncRNA-disease relationships, especially cancer; (ii) lncRNA annotations with tissue expression profiles and predicted function in five common plants; iii) lncRNAs conservation annotation at transcript level for 23 plant species. NONCODEV6 is accessible through http://www.noncode.org/.
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Affiliation(s)
- Lianhe Zhao
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiajia Wang
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yanyan Li
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tingrui Song
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Yang Wu
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Shuangsang Fang
- Beijing University of Chinese Medicine, Chaoyang District, Beijing 100029, China
| | - Dechao Bu
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Hui Li
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Liang Sun
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Dong Pei
- State Key Laboratory of Tree Genetics and Breeding, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
| | - Yu Zheng
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jianqin Huang
- Nurturing Station for the State Key Laboratory of Subtropical Silviculture, Zhejiang A & F University, Lin'an, Hangzhou 311300, China
| | - Mingqing Xu
- Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education), Shanghai Jiao Tong University, Shanghai 518102, China
| | - Runsheng Chen
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.,Guangdong Geneway Decoding Bio-Tech Co. Ltd, Foshan 528316, China.,National Genomics Data Center, Chinese Academy of Sciences, Beijing 100101, China
| | - Yi Zhao
- Key Laboratory of Intelligent Information Processing, Advanced Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
| | - Shunmin He
- Key Laboratory of RNA Biology, Center for Big Data Research in Health, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China.,College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.,National Genomics Data Center, Chinese Academy of Sciences, Beijing 100101, China
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28
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Ning L, Cui T, Zheng B, Wang N, Luo J, Yang B, Du M, Cheng J, Dou Y, Wang D. MNDR v3.0: mammal ncRNA-disease repository with increased coverage and annotation. Nucleic Acids Res 2021; 49:D160-D164. [PMID: 32833025 PMCID: PMC7779040 DOI: 10.1093/nar/gkaa707] [Citation(s) in RCA: 85] [Impact Index Per Article: 28.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 08/12/2020] [Accepted: 08/14/2020] [Indexed: 02/07/2023] Open
Abstract
Many studies have indicated that non-coding RNA (ncRNA) dysfunction is closely related to numerous diseases. Recently, accumulated ncRNA-disease associations have made related databases insufficient to meet the demands of biomedical research. The constant updating of ncRNA-disease resources has become essential. Here, we have updated the mammal ncRNA-disease repository (MNDR, http://www.rna-society.org/mndr/) to version 3.0, containing more than one million entries, four-fold increment in data compared to the previous version. Experimental and predicted circRNA-disease associations have been integrated, increasing the number of categories of ncRNAs to five, and the number of mammalian species to 11. Moreover, ncRNA-disease related drug annotations and associations, as well as ncRNA subcellular localizations and interactions, were added. In addition, three ncRNA-disease (miRNA/lncRNA/circRNA) prediction tools were provided, and the website was also optimized, making it more practical and user-friendly. In summary, MNDR v3.0 will be a valuable resource for the investigation of disease mechanisms and clinical treatment strategies.
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Affiliation(s)
- Lin Ning
- Dermatology Hospital, Southern Medical University, Guangzhou 510091, China
| | - Tianyu Cui
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Boyang Zheng
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Nuo Wang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Jiaxin Luo
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Beilei Yang
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Mengze Du
- Qingyuan People's Hospital, The Sixth Affiliated Hospital of Guangzhou Medical University, B24 Yinquan South Road, Qingyuan 511518, Guangdong Province, People's Republic of China
| | - Jun Cheng
- Affiliated Foshan Maternity & Child Healthcare Hospital, Southern Medical University (Foshan Maternity & Child Healthcare Hospital)
| | - Yiying Dou
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
| | - Dong Wang
- Dermatology Hospital, Southern Medical University, Guangzhou 510091, China
- Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 611731, China
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29
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Liu T, Chen JM, Zhang D, Zhang Q, Peng B, Xu L, Tang H. ApoPred: Identification of Apolipoproteins and Their Subfamilies With Multifarious Features. Front Cell Dev Biol 2021; 8:621144. [PMID: 33490085 PMCID: PMC7820372 DOI: 10.3389/fcell.2020.621144] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2020] [Accepted: 11/24/2020] [Indexed: 01/24/2023] Open
Abstract
Apolipoprotein is a group of plasma proteins that are associated with a variety of diseases, such as hyperlipidemia, atherosclerosis, Alzheimer’s disease, and diabetes. In order to investigate the function of apolipoproteins and to develop effective targets for related diseases, it is necessary to accurately identify and classify apolipoproteins. Although it is possible to identify apolipoproteins accurately through biochemical experiments, they are expensive and time-consuming. This work aims to establish a high-efficiency and high-accuracy prediction model for recognition of apolipoproteins and their subfamilies. We firstly constructed a high-quality benchmark dataset including 270 apolipoproteins and 535 non-apolipoproteins. Based on the dataset, pseudo-amino acid composition (PseAAC) and composition of k-spaced amino acid pairs (CKSAAP) were used as input vectors. To improve the prediction accuracy and eliminate redundant information, analysis of variance (ANOVA) was used to rank the features. And the incremental feature selection was utilized to obtain the best feature subset. Support vector machine (SVM) was proposed to construct the classification model, which could produce the accuracy of 97.27%, sensitivity of 96.30%, and specificity of 97.76% for discriminating apolipoprotein from non-apolipoprotein in 10-fold cross-validation. In addition, the same process was repeated to generate a new model for predicting apolipoprotein subfamilies. The new model could achieve an overall accuracy of 95.93% in 10-fold cross-validation. According to our proposed model, a convenient webserver called ApoPred was established, which can be freely accessed at http://tang-biolab.com/server/ApoPred/service.html. We expect that this work will contribute to apolipoprotein function research and drug development in relevant diseases.
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Affiliation(s)
- Ting Liu
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, China
| | - Jia-Mao Chen
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, China
| | - Dan Zhang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Qian Zhang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, China
| | - Bowen Peng
- Division of international Cooperation, Health Commission of Sichuan Province, Chengdu, China
| | - Lei Xu
- School of Electronic and Communication Engineering, Shenzhen Polytechnic, Shenzhen, China
| | - Hua Tang
- School of Basic Medical Sciences, Southwest Medical University, Luzhou, China.,Central Nervous System Drug Key Laboratory of Sichuan Province, Luzhou, China
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30
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Gu S, Zhang G, Si Q, Dai J, Song Z, Wang Y. Web tools to perform long non-coding RNAs analysis in oncology research. DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION 2021; 2021:6326500. [PMID: 34296748 PMCID: PMC8299716 DOI: 10.1093/database/baab047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 06/21/2021] [Accepted: 07/11/2021] [Indexed: 11/14/2022]
Abstract
Accumulated evidence suggests that the widely expressed long-non-coding RNAs (lncRNAs) are involved in biogenesis. Some aberrant lncRNAs are closely related to pathological changes, for instance, in cancer. Both in tumorigenesis and cancer progression, depending on the interplay with cellular molecules, lncRNAs can modulate transcriptional interference, chromatin remodeling, post-translational regulation and protein modification, and further interfere with signaling pathways. Aiming to the diagnosis/ prognosis markers or potential therapeutical targets, it is important to figure out the specific mechanism and the tissue-specific expressing patterns of lncRNAs. Generally, the bioinformatics analysis is the first step. More and more in silico databases are increasing. But the existing integrative online platforms’ functions are not only having their unique features but also share some common features, which may lead to a waste of time for researchers. Here, we reviewed these web tools according to the functions. For each database, we clarified the data source, analysis method and the evidence that the analysis result is derived from. This review also illustrated examples in practical use for a specific lncRNA by these web tools. It will provide convenience for researchers to quickly choose the appropriate bioinformatics web tools in oncology studies.
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Affiliation(s)
- Shixing Gu
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, No.1166 Liutai Road, Chengdu, Sichuan 611137, China
| | - Guangjie Zhang
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, No.1166 Liutai Road, Chengdu, Sichuan 611137, China.,Department of Clinical Laboratory, Chengdu Fifth People's Hospital, No.33 Mashi Street, Chengdu, Sichuan 611130, China
| | - Qin Si
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, No.1166 Liutai Road, Chengdu, Sichuan 611137, China
| | - Jiawen Dai
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, No.1166 Liutai Road, Chengdu, Sichuan 611137, China
| | - Zhen Song
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, No.1166 Liutai Road, Chengdu, Sichuan 611137, China
| | - Yingshuang Wang
- College of Medical Technology, Chengdu University of Traditional Chinese Medicine, No.1166 Liutai Road, Chengdu, Sichuan 611137, China
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31
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Yan P, Pang P, Hu X, Wang A, Zhang H, Ma Y, Zhang K, Ye Y, Zhou B, Mao J. Specific MiRNAs in naïve T cells associated with Hepatitis C Virus-induced Hepatocellular Carcinoma. J Cancer 2021; 12:1-9. [PMID: 33391397 PMCID: PMC7738825 DOI: 10.7150/jca.49594] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2020] [Accepted: 10/10/2020] [Indexed: 12/16/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the fifth most common type of cancer and the second leading cause of cancer-associated mortality worldwide. Hepatitis C virus (HCV) infection is the primary cause of hepatic fibrosis and cirrhosis, which in turn, notably increase the risk of developing HCC. The systematic immune response plays a vital role in protecting eukaryotic cells from exogenous antigens. In the present study, to determine the association between T cells and miRNAs in HCV-induced HCC (HCV-HCC), bulk mRNA and miRNA sequencing data from HCV-HCC tissues were combined, along with single-cell RNA sequencing (RNA-seq) data from T cells. Deconvoluted bulk RNA-seq data and miRNA profiles enabled the identification of naive CD4+ T cell-associated miRNAs, which may help to elucidate the underlying mechanism of the anti-HCV immune response. Using bulk RNA-seq data, the current analysis presents a feasible method for assessing the relationship between miRNAs and cell components, providing valuable insights into the effects of T cell-associated miRNAs in HCV-HCC.
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Affiliation(s)
- Peng Yan
- Center for Interventional Medicine, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
- Guangdong Provincial Key Laboratory of Biomedical Imaging, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
| | - Pengfei Pang
- Center for Interventional Medicine, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
- Guangdong Provincial Key Laboratory of Biomedical Imaging, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
- Guangdong Provincial Engineering Research Center of Molecular Imaging, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
| | - Xiaojun Hu
- Center for Interventional Medicine, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
- Guangdong Provincial Key Laboratory of Biomedical Imaging, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
- Guangdong Provincial Engineering Research Center of Molecular Imaging, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
| | - Ani Wang
- Department of Cardiovascular Medicine, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000, P.R. China
| | - Huitao Zhang
- Center for Interventional Medicine, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
- Guangdong Provincial Key Laboratory of Biomedical Imaging, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
| | - Yingdong Ma
- Center for Interventional Medicine, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
| | - Ke Zhang
- Center for Interventional Medicine, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
- Guangdong Provincial Key Laboratory of Biomedical Imaging, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
- Guangdong Provincial Engineering Research Center of Molecular Imaging, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
| | - Yaochao Ye
- Center for Interventional Medicine, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
- Guangdong Provincial Key Laboratory of Biomedical Imaging, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
| | - Bin Zhou
- Center for Interventional Medicine, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
- Guangdong Provincial Key Laboratory of Biomedical Imaging, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
- Guangdong Provincial Engineering Research Center of Molecular Imaging, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
| | - Junjie Mao
- Center for Interventional Medicine, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
- Guangdong Provincial Key Laboratory of Biomedical Imaging, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
- Guangdong Provincial Engineering Research Center of Molecular Imaging, the Fifth Affiliated Hospital, Sun Yat-sen University, Zhuhai, Guangdong 519000
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32
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Xiao Y, Xiao Z, Feng X, Chen Z, Kuang L, Wang L. A novel computational model for predicting potential LncRNA-disease associations based on both direct and indirect features of LncRNA-disease pairs. BMC Bioinformatics 2020; 21:555. [PMID: 33267800 PMCID: PMC7709313 DOI: 10.1186/s12859-020-03906-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Accepted: 11/25/2020] [Indexed: 12/25/2022] Open
Abstract
Background Accumulating evidence has demonstrated that long non-coding RNAs (lncRNAs) are closely associated with human diseases, and it is useful for the diagnosis and treatment of diseases to get the relationships between lncRNAs and diseases. Due to the high costs and time complexity of traditional bio-experiments, in recent years, more and more computational methods have been proposed by researchers to infer potential lncRNA-disease associations. However, there exist all kinds of limitations in these state-of-the-art prediction methods as well. Results In this manuscript, a novel computational model named FVTLDA is proposed to infer potential lncRNA-disease associations. In FVTLDA, its major novelty lies in the integration of direct and indirect features related to lncRNA-disease associations such as the feature vectors of lncRNA-disease pairs and their corresponding association probability fractions, which guarantees that FVTLDA can be utilized to predict diseases without known related-lncRNAs and lncRNAs without known related-diseases. Moreover, FVTLDA neither relies solely on known lncRNA-disease nor requires any negative samples, which guarantee that it can infer potential lncRNA-disease associations more equitably and effectively than traditional state-of-the-art prediction methods. Additionally, to avoid the limitations of single model prediction techniques, we combine FVTLDA with the Multiple Linear Regression (MLR) and the Artificial Neural Network (ANN) for data analysis respectively. Simulation experiment results show that FVTLDA with MLR can achieve reliable AUCs of 0.8909, 0.8936 and 0.8970 in 5-Fold Cross Validation (fivefold CV), 10-Fold Cross Validation (tenfold CV) and Leave-One-Out Cross Validation (LOOCV), separately, while FVTLDA with ANN can achieve reliable AUCs of 0.8766, 0.8830 and 0.8807 in fivefold CV, tenfold CV, and LOOCV respectively. Furthermore, in case studies of gastric cancer, leukemia and lung cancer, experiment results show that there are 8, 8 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with MLR, and 8, 7 and 8 out of top 10 candidate lncRNAs predicted by FVTLDA with ANN, having been verified by recent literature. Comparing with the representative prediction model of KATZLDA, comparison results illustrate that FVTLDA with MLR and FVTLDA with ANN can achieve the average case study contrast scores of 0.8429 and 0.8515 respectively, which are both notably higher than the average case study contrast score of 0.6375 achieved by KATZLDA. Conclusion The simulation results show that FVTLDA has good prediction performance, which is a good supplement to future bioinformatics research.
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Affiliation(s)
- Yubin Xiao
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410001, People's Republic of China.,Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, People's Republic of China
| | - Zheng Xiao
- Hunan Province Key Laboratory of Tumor Cellular and Molecular Pathology, Cancer Research Institute, University of South China, Hengyang, 421001, Hunan, People's Republic of China
| | - Xiang Feng
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410001, People's Republic of China
| | - Zhiping Chen
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410001, People's Republic of China
| | - Linai Kuang
- Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, People's Republic of China
| | - Lei Wang
- College of Computer Engineering and Applied Mathematics, Changsha University, Changsha, 410001, People's Republic of China. .,Key Laboratory of Hunan Province for Internet of Things and Information Security, Xiangtan University, Xiangtan, 411105, People's Republic of China.
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33
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Zheng M, Mullikin H, Hester A, Czogalla B, Heidegger H, Vilsmaier T, Vattai A, Chelariu-Raicu A, Jeschke U, Trillsch F, Mahner S, Kaltofen T. Development and Validation of a Novel 11-Gene Prognostic Model for Serous Ovarian Carcinomas Based on Lipid Metabolism Expression Profile. Int J Mol Sci 2020; 21:E9169. [PMID: 33271935 PMCID: PMC7731240 DOI: 10.3390/ijms21239169] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2020] [Revised: 11/06/2020] [Accepted: 11/27/2020] [Indexed: 02/06/2023] Open
Abstract
(1) Background: Biomarkers might play a significant role in predicting the clinical outcomes of patients with ovarian cancer. By analyzing lipid metabolism genes, future perspectives may be uncovered; (2) Methods: RNA-seq data for serous ovarian cancer were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. The non-negative matrix factorization package in programming language R was used to classify molecular subtypes of lipid metabolism genes and the limma package in R was performed for functional enrichment analysis. Through lasso regression, we constructed a multi-gene prognosis model; (3) Results: Two molecular subtypes were obtained and an 11-gene signature was constructed (PI3, RGS, ADORA3, CH25H, CCDC80, PTGER3, MATK, KLRB1, CCL19, CXCL9 and CXCL10). Our prognostic model shows a good independent prognostic ability in ovarian cancer. In a nomogram, the predictive efficiency was notably superior to that of traditional clinical features. Related to known models in ovarian cancer with a comparable amount of genes, ours has the highest concordance index; (4) Conclusions: We propose an 11-gene signature prognosis prediction model based on lipid metabolism genes in serous ovarian cancer.
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Affiliation(s)
- Mingjun Zheng
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Heather Mullikin
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Anna Hester
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Bastian Czogalla
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Helene Heidegger
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Theresa Vilsmaier
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Aurelia Vattai
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Anca Chelariu-Raicu
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Udo Jeschke
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
- Department of Obstetrics and Gynecology, University Hospital Augsburg, Stenglinstrasse 2, 86156 Augsburg, Germany
| | - Fabian Trillsch
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Sven Mahner
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
| | - Till Kaltofen
- Department of Obstetrics and Gynecology, University Hospital, LMU Munich, Maistrasse 11, 80337 Munich, Germany; (M.Z.); (H.M.); (A.H.); (B.C.); (H.H.); (T.V.); (A.V.); (A.C.-R.); (U.J.); (F.T.); (S.M.)
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Kern F, Fehlmann T, Solomon J, Schwed L, Grammes N, Backes C, Van Keuren-Jensen K, Craig DW, Meese E, Keller A. miEAA 2.0: integrating multi-species microRNA enrichment analysis and workflow management systems. Nucleic Acids Res 2020; 48:W521-W528. [PMID: 32374865 PMCID: PMC7319446 DOI: 10.1093/nar/gkaa309] [Citation(s) in RCA: 123] [Impact Index Per Article: 30.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2020] [Revised: 04/06/2020] [Accepted: 04/22/2020] [Indexed: 01/01/2023] Open
Abstract
Gene set enrichment analysis has become one of the most frequently used applications in molecular biology research. Originally developed for gene sets, the same statistical principles are now available for all omics types. In 2016, we published the miRNA enrichment analysis and annotation tool (miEAA) for human precursor and mature miRNAs. Here, we present miEAA 2.0, supporting miRNA input from ten frequently investigated organisms. To facilitate inclusion of miEAA in workflow systems, we implemented an Application Programming Interface (API). Users can perform miRNA set enrichment analysis using either the web-interface, a dedicated Python package, or custom remote clients. Moreover, the number of category sets was raised by an order of magnitude. We implemented novel categories like annotation confidence level or localisation in biological compartments. In combination with the miRBase miRNA-version and miRNA-to-precursor converters, miEAA supports research settings where older releases of miRBase are in use. The web server also offers novel comprehensive visualizations such as heatmaps and running sum curves with background distributions. We demonstrate the new features with case studies for human kidney cancer, a biomarker study on Parkinson’s disease from the PPMI cohort, and a mouse model for breast cancer. The tool is freely accessible at: https://www.ccb.uni-saarland.de/mieaa2.
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Affiliation(s)
- Fabian Kern
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Tobias Fehlmann
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Jeffrey Solomon
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Louisa Schwed
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Nadja Grammes
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | - Christina Backes
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany
| | | | - David Wesley Craig
- Institute of Translational Genomics, University of Southern California, Los Angeles, CA 90033, USA
| | - Eckart Meese
- Department of Human Genetics, Saarland University, 66421 Homburg, Germany
| | - Andreas Keller
- Chair for Clinical Bioinformatics, Saarland University, 66123 Saarbrücken, Germany.,School of Medicine Office, Stanford University, Stanford, CA 94305, USA.,Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94304, USA
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35
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Xiong C, Sun S, Jiang W, Ma L, Zhang J. ASDmiR: A Stepwise Method to Uncover miRNA Regulation Related to Autism Spectrum Disorder. Front Genet 2020; 11:562971. [PMID: 33173536 PMCID: PMC7591752 DOI: 10.3389/fgene.2020.562971] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2020] [Accepted: 08/31/2020] [Indexed: 12/14/2022] Open
Abstract
Autism spectrum disorder (ASD) is a class of neurodevelopmental disorders characterized by genetic and environmental risk factors. The pathogenesis of ASD has a strong genetic basis, consisting of rare de novo or inherited variants among a variety of multiple molecules. Previous studies have shown that microRNAs (miRNAs) are involved in neurogenesis and brain development and are closely associated with the pathogenesis of ASD. However, the regulatory mechanisms of miRNAs in ASD are largely unclear. In this work, we present a stepwise method, ASDmiR, for the identification of underlying pathogenic genes, networks, and modules associated with ASD. First, we conduct a comparison study on 12 miRNA target prediction methods by using the matched miRNA, lncRNA, and mRNA expression data in ASD. In terms of the number of experimentally confirmed miRNA-target interactions predicted by each method, we choose the best method for identifying miRNA-target regulatory network. Based on the miRNA-target interaction network identified by the best method, we further infer miRNA-target regulatory bicliques or modules. In addition, by integrating high-confidence miRNA-target interactions and gene expression data, we identify three types of networks, including lncRNA-lncRNA, lncRNA-mRNA, and mRNA-mRNA related miRNA sponge interaction networks. To reveal the community of miRNA sponges, we further infer miRNA sponge modules from the identified miRNA sponge interaction network. Functional analysis results show that the identified hub genes, as well as miRNA-associated networks and modules, are closely linked with ASD. ASDmiR is freely available at https://github.com/chenchenxiong/ASDmiR.
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Affiliation(s)
- Chenchen Xiong
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
| | - Shaoping Sun
- Department of Medical Engineering, People's Hospital of Yuxi City, Yuxi, China
| | - Weili Jiang
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
| | - Lei Ma
- Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, China
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36
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Kang J, Yao P, Tang Q, Wang Y, Zhou Y, Huang J. Systematic Analysis of Competing Endogenous RNA Networks in Diffuse Large B-Cell Lymphoma and Hodgkin's Lymphoma. Front Genet 2020; 11:586688. [PMID: 33193722 PMCID: PMC7554339 DOI: 10.3389/fgene.2020.586688] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2020] [Accepted: 08/28/2020] [Indexed: 12/19/2022] Open
Abstract
Lymphoma is a systemic malignancy, originating from the lymphatic system, which accounts for 3 to 4% of all tumors. There are two major subtypes of lymphoma, namely, diffuse large B-cell lymphoma (DLBCL) and Hodgkin’s lymphoma (HL). Elucidation of the pathogenesis of these two lymphoma types is crucial for the identification of potential therapeutic targets. Compared with the corresponding knowledge of other diseases, the understanding of the regulatory networks involved in DLBCL and HL is relatively deficient. To address this, we comprehensively analyzed the mRNAs, lncRNAs, and miRNAs that were differentially expressed between normal and tumor samples of DLBCL and HL. In addition, functional enrichment analysis of the differentially expressed mRNAs was performed. We constructed two specific ceRNA networks of DLBCL and HL. The pathways enriched by dysregulated mRNAs in DLBCL and HL were mainly involved in immune responses, transcription process, and metabolism process. The ceRNA network analysis revealed that 45 ceRNAs were shared between the two ceRNA networks, including five pivotal lncRNAs (MALAT1, CTBP1-AS, THUMPD3-AS, PSMA3-AS1, and NUTM2A-AS1). In addition, we proposed a DLBCL survival risk model based on a DLBCL-specific network constructed by Lasso regression analysis. The model, which is based on eight mRNAs, exhibited excellent performance in regard to predicting outcomes in DLBCL patients, with a p value of 0.0017 and AUC of 0.9783. In summary, although the molecular mechanisms underlying tumorigenesis in DLBCL and HL were quite different, the same pivotal lncRNAs acted as key regulators. Our findings identify novel potential prognostic and therapeutic targets for DLBCL and HL.
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Affiliation(s)
- Juanjuan Kang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China.,Affiliated Foshan Maternity & Child Healthcare Hospital, Southern Medical University, Foshan, China
| | - Pengcheng Yao
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Qiang Tang
- Innovative Institute of Chinese Medicine and Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu, China
| | - Ying Wang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuwei Zhou
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu, China
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Calanca N, Abildgaard C, Rainho CA, Rogatto SR. The Interplay between Long Noncoding RNAs and Proteins of the Epigenetic Machinery in Ovarian Cancer. Cancers (Basel) 2020; 12:E2701. [PMID: 32967233 PMCID: PMC7563210 DOI: 10.3390/cancers12092701] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 09/09/2020] [Accepted: 09/16/2020] [Indexed: 12/19/2022] Open
Abstract
Comprehensive large-scale sequencing and bioinformatics analyses have uncovered a myriad of cancer-associated long noncoding RNAs (lncRNAs). Aberrant expression of lncRNAs is associated with epigenetic reprogramming during tumor development and progression, mainly due to their ability to interact with DNA, RNA, or proteins to regulate gene expression. LncRNAs participate in the control of gene expression patterns during development and cell differentiation and can be cell and cancer type specific. In this review, we described the potential of lncRNAs for clinical applications in ovarian cancer (OC). OC is a complex and heterogeneous disease characterized by relapse, chemoresistance, and high mortality rates. Despite advances in diagnosis and treatment, no significant improvements in long-term survival were observed in OC patients. A set of lncRNAs was associated with survival and response to therapy in this malignancy. We manually curated databases and used bioinformatics tools to identify lncRNAs implicated in the epigenetic regulation, along with examples of direct interactions between the lncRNAs and proteins of the epigenetic machinery in OC. The resources and mechanisms presented herein can improve the understanding of OC biology and provide the basis for further investigations regarding the selection of novel biomarkers and therapeutic targets.
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Affiliation(s)
- Naiade Calanca
- Department of Chemical and Biological Sciences, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, Brazil; (N.C.); (C.A.R.)
| | - Cecilie Abildgaard
- Department of Oncology, University Hospital of Southern Denmark-Vejle, Institute of Regional Health Research, University of Southern Denmark, 5000 Odense, Denmark;
- Department of Clinical Genetics, University Hospital of Southern Denmark-Vejle, Institute of Regional Health Research, University of Southern Denmark, 5000 Odense, Denmark
| | - Cláudia Aparecida Rainho
- Department of Chemical and Biological Sciences, Institute of Biosciences, São Paulo State University (UNESP), Botucatu 18618-689, Brazil; (N.C.); (C.A.R.)
| | - Silvia Regina Rogatto
- Department of Clinical Genetics, University Hospital of Southern Denmark-Vejle, Institute of Regional Health Research, University of Southern Denmark, 5000 Odense, Denmark
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Non-Coding RNA Databases in Cardiovascular Research. Noncoding RNA 2020; 6:ncrna6030035. [PMID: 32887511 PMCID: PMC7549374 DOI: 10.3390/ncrna6030035] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 08/28/2020] [Accepted: 09/01/2020] [Indexed: 12/11/2022] Open
Abstract
Cardiovascular diseases (CVDs) are of multifactorial origin and can be attributed to several genetic and environmental components. CVDs are the leading cause of mortality worldwide and they primarily damage the heart and the vascular system. Non-coding RNA (ncRNA) refers to functional RNA molecules, which have been transcribed into DNA but do not further get translated into proteins. Recent transcriptomic studies have identified the presence of thousands of ncRNA molecules across species. In humans, less than 2% of the total genome represents the protein-coding genes. While the role of many ncRNAs is yet to be ascertained, some long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) have been associated with disease progression, serving as useful diagnostic and prognostic biomarkers. A plethora of data repositories specialized in ncRNAs have been developed over the years using publicly available high-throughput data from next-generation sequencing and other approaches, that cover various facets of ncRNA research like basic and functional annotation, expressional profile, structural and molecular changes, and interaction with other biomolecules. Here, we provide a compendium of the current ncRNA databases relevant to cardiovascular research.
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Zhang W, Zhang H, Yang H, Li M, Xie Z, Li W. Computational resources associating diseases with genotypes, phenotypes and exposures. Brief Bioinform 2020; 20:2098-2115. [PMID: 30102366 PMCID: PMC6954426 DOI: 10.1093/bib/bby071] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Revised: 07/01/2018] [Indexed: 12/16/2022] Open
Abstract
The causes of a disease and its therapies are not only related to genotypes, but also associated with other factors, including phenotypes, environmental exposures, drugs and chemical molecules. Distinguishing disease-related factors from many neutral factors is critical as well as difficult. Over the past two decades, bioinformaticians have developed many computational resources to integrate the omics data and discover associations among these factors. However, researchers and clinicians are experiencing difficulties in choosing appropriate resources from hundreds of relevant databases and software tools. Here, in order to assist the researchers and clinicians, we systematically review the public computational resources of human diseases related to genotypes, phenotypes, environment factors, drugs and chemical exposures. We briefly describe the development history of these computational resources, followed by the details of the relevant databases and software tools. We finally conclude with a discussion of current challenges and future opportunities as well as prospects on this topic.
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Affiliation(s)
- Wenliang Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
| | - Haiyue Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
| | - Huan Yang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
| | - Miaoxin Li
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
| | - Zhi Xie
- State Key Lab of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 500040, China
| | - Weizhong Li
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China
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40
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Garcia-Moreno A, Carmona-Saez P. Computational Methods and Software Tools for Functional Analysis of miRNA Data. Biomolecules 2020; 10:biom10091252. [PMID: 32872205 PMCID: PMC7563698 DOI: 10.3390/biom10091252] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 08/24/2020] [Accepted: 08/26/2020] [Indexed: 12/15/2022] Open
Abstract
miRNAs are important regulators of gene expression that play a key role in many biological processes. High-throughput techniques allow researchers to discover and characterize large sets of miRNAs, and enrichment analysis tools are becoming increasingly important in decoding which miRNAs are implicated in biological processes. Enrichment analysis of miRNA targets is the standard technique for functional analysis, but this approach carries limitations and bias; alternatives are currently being proposed, based on direct and curated annotations. In this review, we describe the two workflows of miRNAs enrichment analysis, based on target gene or miRNA annotations, highlighting statistical tests, software tools, up-to-date databases, and functional annotations resources in the study of metazoan miRNAs.
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Affiliation(s)
- Adrian Garcia-Moreno
- Bioinformatics Unit, Centre for Genomics and Oncological Research (GENyO)—Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, 18016 Granada, Spain;
| | - Pedro Carmona-Saez
- Bioinformatics Unit, Centre for Genomics and Oncological Research (GENyO)—Pfizer/University of Granada/Andalusian Regional Government, PTS Granada, 18016 Granada, Spain;
- Department of Statistics, University of Granada, 18071 Granada, Spain
- Correspondence:
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Fan W, Shang J, Li F, Sun Y, Yuan S, Liu JX. IDSSIM: an lncRNA functional similarity calculation model based on an improved disease semantic similarity method. BMC Bioinformatics 2020; 21:339. [PMID: 32736513 PMCID: PMC7430881 DOI: 10.1186/s12859-020-03699-9] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Accepted: 07/23/2020] [Indexed: 12/17/2022] Open
Abstract
Background It has been widely accepted that long non-coding RNAs (lncRNAs) play important roles in the development and progression of human diseases. Many association prediction models have been proposed for predicting lncRNA functions and identifying potential lncRNA-disease associations. Nevertheless, among them, little effort has been attempted to measure lncRNA functional similarity, which is an essential part of association prediction models. Results In this study, we presented an lncRNA functional similarity calculation model, IDSSIM for short, based on an improved disease semantic similarity method, highlight of which is the introduction of information content contribution factor into the semantic value calculation to take into account both the hierarchical structures of disease directed acyclic graphs and the disease specificities. IDSSIM and three state-of-the-art models, i.e., LNCSIM1, LNCSIM2, and ILNCSIM, were evaluated by applying their disease semantic similarity matrices and the lncRNA functional similarity matrices, as well as corresponding matrices of human lncRNA-disease associations coming from either lncRNADisease database or MNDR database, into an association prediction method WKNKN for lncRNA-disease association prediction. In addition, case studies of breast cancer and adenocarcinoma were also performed to validate the effectiveness of IDSSIM. Conclusions Results demonstrated that in terms of ROC curves and AUC values, IDSSIM is superior to compared models, and can improve accuracy of disease semantic similarity effectively, leading to increase the association prediction ability of the IDSSIM-WKNKN model; in terms of case studies, most of potential disease-associated lncRNAs predicted by IDSSIM can be confirmed by databases and literatures, implying that IDSSIM can serve as a promising tool for predicting lncRNA functions, identifying potential lncRNA-disease associations, and pre-screening candidate lncRNAs to perform biological experiments. The IDSSIM code, all experimental data and prediction results are available online at https://github.com/CDMB-lab/IDSSIM.
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Affiliation(s)
- Wenwen Fan
- School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China
| | - Junliang Shang
- School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China.
| | - Feng Li
- School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China
| | - Yan Sun
- School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China
| | - Shasha Yuan
- School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China
| | - Jin-Xing Liu
- School of Information Science and Engineering, Qufu Normal University, Rizhao, 276826, China
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Straumfors A, Duale N, Foss OAH, Mollerup S. Circulating miRNAs as molecular markers of occupational grain dust exposure. Sci Rep 2020; 10:11317. [PMID: 32647120 PMCID: PMC7347934 DOI: 10.1038/s41598-020-68296-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 06/18/2020] [Indexed: 12/15/2022] Open
Abstract
Dust from grain and feed production may cause adverse health effects in exposed workers. In this study we explored circulating miRNAs as potential biomarkers of occupational grain dust exposure. Twenty-two serum miRNAs were analyzed in 44 grain dust exposed workers and 22 controls. Exposed workers had significantly upregulated miR-18a-5p, miR-124-3p and miR-574-3p, and downregulated miR-19b-3p and miR-146a-5p, compared to controls. Putative target genes for the differentially expressed miRNAs were involved in a range of Kyoto Encyclopedia of Genes and Genomes signaling pathways, and ‘Pathways in cancer’ and ‘Wnt signaling pathway’ were common for all the five miRNAs. MiRNA-diseases association analysis showed a link between the five identified miRNAs and several lung diseases terms. A positive correlation between miR-124-3p, miR-18a-5p, and miR-574-3p and IL-6 protein level was shown, while miR-19b-3p was inversely correlated with CC-16 and sCD40L protein levels. Receiver-operating characteristic analysis of the five miRNA showed that three miRNAs (miR-574-3p, miR-124-3p and miR-18a-5p) could distinguish the grain dust exposed group from the control group, with miR-574-3p as the strongest predictor of grain dust exposure. In conclusion, this study identified five signature miRNAs as potential novel biomarkers of grain dust exposure that may have potential as early disease markers.
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Affiliation(s)
- Anne Straumfors
- National Institute of Occupational Health, Gydas vei 8, PO Box 5330, 0304, Majorstuen, Oslo, Norway.
| | - Nur Duale
- Department of Molecular Biology, Norwegian Institute of Public Health, PO Box 222, 0213, Skøyen, Oslo, Norway
| | - Oda A H Foss
- National Institute of Occupational Health, Gydas vei 8, PO Box 5330, 0304, Majorstuen, Oslo, Norway
| | - Steen Mollerup
- National Institute of Occupational Health, Gydas vei 8, PO Box 5330, 0304, Majorstuen, Oslo, Norway
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43
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Zhang W, Yao G, Wang J, Yang M, Wang J, Zhang H, Li W. ncRPheno: a comprehensive database platform for identification and validation of disease related noncoding RNAs. RNA Biol 2020; 17:943-955. [PMID: 32122231 PMCID: PMC7549653 DOI: 10.1080/15476286.2020.1737441] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Revised: 02/24/2020] [Accepted: 02/25/2020] [Indexed: 12/31/2022] Open
Abstract
Noncoding RNAs (ncRNAs) play critical roles in many critical biological processes and have become a novel class of potential targets and bio-markers for disease diagnosis, therapy, and prognosis. Annotating and analysing ncRNA-disease association data are essential but challenging. Current computational resources lack comprehensive database platforms to consistently interpret and prioritize ncRNA-disease association data for biomedical investigation and application. Here, we present the ncRPheno database platform (http://lilab2.sysu.edu.cn/ncrpheno), which comprehensively integrates and annotates ncRNA-disease association data and provides novel searches, visualizations, and utilities for association identification and validation. ncRPheno contains 482,751 non-redundant associations between 14,494 ncRNAs and 3,210 disease phenotypes across 11 species with supporting evidence in the literature. A scoring model was refined to prioritize the associations based on evidential metrics. Moreover, ncRPheno provides user-friendly web interfaces, novel visualizations, and programmatic access to enable easy exploration, analysis, and utilization of the association data. A case study through ncRPheno demonstrated a comprehensive landscape of ncRNAs dysregulation associated with 22 cancers and uncovered 821 cancer-associated common ncRNAs. As a unique database platform, ncRPheno outperforms the existing similar databases in terms of data coverage and utilities, and it will assist studies in encoding ncRNAs associated with phenotypes ranging from genetic disorders to complex diseases. ABBREVIATIONS APIs: application programming interfaces; circRNA: circular RNA; ECO: Evidence & Conclusion Ontology; EFO: Experimental Factor Ontology; FDR: false discovery rate; GO: Gene Ontology; GWAS: genome wide association studies; HPO: Human Phenotype Ontology; ICGC: International Cancer Genome Consortium; lncRNA: long noncoding RNA; miRNA: micro RNA; ncRNA: noncoding RNA; NGS: next generation sequencing; OMIM: Online Mendelian Inheritance in Man; piRNA: piwi-interacting RNA; snoRNA: small nucleolar RNA; TCGA: The Cancer Genome Atlas.
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Affiliation(s)
- Wenliang Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Guocai Yao
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Jianbo Wang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Minglei Yang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Jing Wang
- School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China
| | - Haiyue Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Weizhong Li
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
- Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China
- Key Laboratory of Tropical Disease Control, Sun Yat-Sen University, Ministry of Education, China
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Luo ZH, Shi MW, Yang Z, Zhang HY, Chen ZX. pyMeSHSim: an integrative python package for biomedical named entity recognition, normalization, and comparison of MeSH terms. BMC Bioinformatics 2020; 21:252. [PMID: 32552728 PMCID: PMC7301509 DOI: 10.1186/s12859-020-03583-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Accepted: 06/04/2020] [Indexed: 01/24/2023] Open
Abstract
Background Many disease causing genes have been identified through different methods, but there have been no uniform annotations of biomedical named entity (bio-NE) of the disease phenotypes of these genes yet. Furthermore, semantic similarity comparison between two bio-NE annotations has become important for data integration or system genetics analysis. Results The package pyMeSHSim recognizes bio-NEs by using MetaMap which produces Unified Medical Language System (UMLS) concepts in natural language process. To map the UMLS concepts to Medical Subject Headings (MeSH), pyMeSHSim is embedded with a house-made dataset containing the main headings (MHs), supplementary concept records (SCRs), and their relations in MeSH. Based on the dataset, pyMeSHSim implemented four information content (IC)-based algorithms and one graph-based algorithm to measure the semantic similarity between two MeSH terms. To evaluate its performance, we used pyMeSHSim to parse OMIM and GWAS phenotypes. The pyMeSHSim introduced SCRs and the curation strategy of non-MeSH-synonymous UMLS concepts, which improved the performance of pyMeSHSim in the recognition of OMIM phenotypes. In the curation of 461 GWAS phenotypes, pyMeSHSim showed recall > 0.94, precision > 0.56, and F1 > 0.70, demonstrating better performance than the state-of-the-art tools DNorm and TaggerOne in recognizing MeSH terms from short biomedical phrases. The semantic similarity in MeSH terms recognized by pyMeSHSim and the previous manual work was calculated by pyMeSHSim and another semantic analysis tool meshes, respectively. The result indicated that the correlation of semantic similarity analysed by two tools reached as high as 0.89–0.99. Conclusions The integrative MeSH tool pyMeSHSim embedded with the MeSH MHs and SCRs realized the bio-NE recognition, normalization, and comparison in biomedical text-mining.
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Affiliation(s)
- Zhi-Hui Luo
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, 430070, PR China.,College of Biomedicine and Health, Huazhong Agricultural University, Wuhan, Hubei, 430070, PR China
| | - Meng-Wei Shi
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, 430070, PR China.,College of Biomedicine and Health, Huazhong Agricultural University, Wuhan, Hubei, 430070, PR China
| | - Zhuang Yang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, 430070, PR China.,College of Biomedicine and Health, Huazhong Agricultural University, Wuhan, Hubei, 430070, PR China
| | - Hong-Yu Zhang
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Informatics, Huazhong Agricultural University, Wuhan, Hubei, 430070, PR China.
| | - Zhen-Xia Chen
- Hubei Key Laboratory of Agricultural Bioinformatics, College of Life Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, 430070, PR China. .,College of Biomedicine and Health, Huazhong Agricultural University, Wuhan, Hubei, 430070, PR China.
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45
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Yan C, Zhang Z, Bao S, Hou P, Zhou M, Xu C, Sun J. Computational Methods and Applications for Identifying Disease-Associated lncRNAs as Potential Biomarkers and Therapeutic Targets. MOLECULAR THERAPY. NUCLEIC ACIDS 2020; 21:156-171. [PMID: 32585624 PMCID: PMC7321789 DOI: 10.1016/j.omtn.2020.05.018] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 04/06/2020] [Accepted: 05/18/2020] [Indexed: 12/12/2022]
Abstract
Long non-coding RNAs (lncRNAs) have been recognized as critical components of a broad genomic regulatory network and play pivotal roles in physiological and pathological processes. Identification of disease-associated lncRNAs is becoming increasingly crucial for fundamentally improving our understanding of molecular mechanisms of disease and developing novel biomarkers and therapeutic targets. Considering lower efficiency and higher time and labor cost of biological experiments, computer-aided inference of disease-associated RNAs has become a promising avenue for facilitating the study of lncRNA functions and provides complementary value for experimental studies. In this study, we first summarize data and knowledge resources publicly available for the study of lncRNA-disease associations. Then, we present an updated systematic overview of dozens of computational methods and models for inferring lncRNA-disease associations proposed in recent years. Finally, we explore the perspectives and challenges for further studies. Our study provides a guide for biologists and medical scientists to look for dedicated resources and more competent tools for accelerating the unraveling of disease-associated lncRNAs.
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Affiliation(s)
- Congcong Yan
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P.R. China
| | - Zicheng Zhang
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P.R. China
| | - Siqi Bao
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P.R. China
| | - Ping Hou
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P.R. China
| | - Meng Zhou
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P.R. China
| | - Chongyong Xu
- Department of Radiology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou 325027, P.R. China.
| | - Jie Sun
- School of Biomedical Engineering, School of Ophthalmology & Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, P.R. China.
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46
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Liu D, Zhao L, Wang Z, Zhou X, Fan X, Li Y, Xu J, Hu S, Niu M, Song X, Li Y, Zuo L, Lei C, Zhang M, Tang G, Huang M, Zhang N, Duan L, Lv H, Zhang M, Li J, Xu L, Kong F, Feng R, Jiang Y. EWASdb: epigenome-wide association study database. Nucleic Acids Res 2020; 47:D989-D993. [PMID: 30321400 PMCID: PMC6323898 DOI: 10.1093/nar/gky942] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 10/04/2018] [Indexed: 12/29/2022] Open
Abstract
DNA methylation, the most intensively studied epigenetic modification, plays an important role in understanding the molecular basis of diseases. Furthermore, epigenome-wide association study (EWAS) provides a systematic approach to identify epigenetic variants underlying common diseases/phenotypes. However, there is no comprehensive database to archive the results of EWASs. To fill this gap, we developed the EWASdb, which is a part of 'The EWAS Project', to store the epigenetic association results of DNA methylation from EWASs. In its current version (v 1.0, up to July 2018), the EWASdb has curated 1319 EWASs associated with 302 diseases/phenotypes. There are three types of EWAS results curated in this database: (i) EWAS for single marker; (ii) EWAS for KEGG pathway and (iii) EWAS for GO (Gene Ontology) category. As the first comprehensive EWAS database, EWASdb has been searched or downloaded by researchers from 43 countries to date. We believe that EWASdb will become a valuable resource and significantly contribute to the epigenetic research of diseases/phenotypes and have potential clinical applications. EWASdb is freely available at http://www.ewas.org.cn/ewasdb or http://www.bioapp.org/ewasdb.
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Affiliation(s)
- Di Liu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China
| | - Linna Zhao
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China
| | - Zhaoyang Wang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China
| | - Xu Zhou
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China
| | - Xiuzhao Fan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China
| | - Yong Li
- Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China
| | - Jing Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China
| | - Simeng Hu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China
| | - Miaomiao Niu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China
| | - Xiuling Song
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China
| | - Ying Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China
| | - Lijiao Zuo
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China
| | - Changgui Lei
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China
| | - Meng Zhang
- Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China.,Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, China
| | - Guoping Tang
- The Fourth Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang, China
| | - Min Huang
- Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China.,Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, China
| | - Nan Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China
| | - Lian Duan
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Hongchao Lv
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Mingming Zhang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Jin Li
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
| | - Liangde Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China
| | - Fanwu Kong
- Department of Nephrology, The Second Affiliated Hospital, Harbin Medical University, Harbin, China
| | - Rennan Feng
- Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China.,Department of Nutrition and Food Hygiene, Public Health College, Harbin Medical University, Harbin, China
| | - Yongshuai Jiang
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.,Training Center for Students Innovation and Entrepreneurship Education, Harbin Medical University, Harbin, China
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47
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Volders PJ, Anckaert J, Verheggen K, Nuytens J, Martens L, Mestdagh P, Vandesompele J. LNCipedia 5: towards a reference set of human long non-coding RNAs. Nucleic Acids Res 2020; 47:D135-D139. [PMID: 30371849 PMCID: PMC6323963 DOI: 10.1093/nar/gky1031] [Citation(s) in RCA: 337] [Impact Index Per Article: 84.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Accepted: 10/17/2018] [Indexed: 12/20/2022] Open
Abstract
While long non-coding RNA (lncRNA) research in the past has primarily focused on the discovery of novel genes, today it has shifted towards functional annotation of this large class of genes. With thousands of lncRNA studies published every year, the current challenge lies in keeping track of which lncRNAs are functionally described. This is further complicated by the fact that lncRNA nomenclature is not straightforward and lncRNA annotation is scattered across different resources with their own quality metrics and definition of a lncRNA. To overcome this issue, large scale curation and annotation is needed. Here, we present the fifth release of the human lncRNA database LNCipedia (https://lncipedia.org). The most notable improvements include manual literature curation of 2482 lncRNA articles and the use of official gene symbols when available. In addition, an improved filtering pipeline results in a higher quality reference lncRNA gene set.
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Affiliation(s)
- Pieter-Jan Volders
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
- Center for Medical Genetics (CMGG), Ghent University, 9000 Ghent, Belgium
- VIB-UGent Center for Medical Biotechnology, 9000 Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium
- To whom correspondence should be addressed. Tel: +32 9 332 6979; Fax: +32 9 332 6549;
| | - Jasper Anckaert
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
- Center for Medical Genetics (CMGG), Ghent University, 9000 Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium
| | - Kenneth Verheggen
- VIB-UGent Center for Medical Biotechnology, 9000 Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium
| | - Justine Nuytens
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
- Center for Medical Genetics (CMGG), Ghent University, 9000 Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, 9000 Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium
| | - Pieter Mestdagh
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
- Center for Medical Genetics (CMGG), Ghent University, 9000 Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium
| | - Jo Vandesompele
- Cancer Research Institute Ghent (CRIG), 9000 Ghent, Belgium
- Center for Medical Genetics (CMGG), Ghent University, 9000 Ghent, Belgium
- Department of Biomolecular Medicine, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium
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48
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Cheng L, Wang P, Tian R, Wang S, Guo Q, Luo M, Zhou W, Liu G, Jiang H, Jiang Q. LncRNA2Target v2.0: a comprehensive database for target genes of lncRNAs in human and mouse. Nucleic Acids Res 2020; 47:D140-D144. [PMID: 30380072 PMCID: PMC6323902 DOI: 10.1093/nar/gky1051] [Citation(s) in RCA: 231] [Impact Index Per Article: 57.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2018] [Accepted: 10/26/2018] [Indexed: 12/12/2022] Open
Abstract
Long non-coding RNAs (lncRNAs) play crucial roles in regulating gene expression, and a growing number of researchers have focused on the identification of target genes of lncRNAs. However, no online repository is available to collect the information on target genes regulated by lncRNAs. To make it convenient for researchers to know what genes are regulated by a lncRNA of interest, we developed a database named lncRNA2Target to provide a comprehensive resource of lncRNA target genes in 2015. To update the database this year, we retrieved all new lncRNA-target relationships from papers published from 1 August 2014 to 30 April 2018 and RNA-seq datasets before and after knockdown or overexpression of a specific lncRNA. LncRNA2Target database v2.0 provides a web interface through which its users can search for the targets of a particular lncRNA or for the lncRNAs that target a particular gene, and is freely accessible at http://123.59.132.21/lncrna2target.
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Affiliation(s)
- Liang Cheng
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Pingping Wang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Rui Tian
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Song Wang
- Department of Radiology, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China
| | - Qinghua Guo
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Meng Luo
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Wenyang Zhou
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Guiyou Liu
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Huijie Jiang
- Department of Radiology, The Second Affiliated Hospital of Harbin Medical University, Harbin 150086, China
| | - Qinghua Jiang
- Center for Bioinformatics, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
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LMSM: A modular approach for identifying lncRNA related miRNA sponge modules in breast cancer. PLoS Comput Biol 2020; 16:e1007851. [PMID: 32324747 PMCID: PMC7200020 DOI: 10.1371/journal.pcbi.1007851] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2019] [Revised: 05/05/2020] [Accepted: 04/06/2020] [Indexed: 12/12/2022] Open
Abstract
Until now, existing methods for identifying lncRNA related miRNA sponge modules mainly rely on lncRNA related miRNA sponge interaction networks, which may not provide a full picture of miRNA sponging activities in biological conditions. Hence there is a strong need of new computational methods to identify lncRNA related miRNA sponge modules. In this work, we propose a framework, LMSM, to identify LncRNA related MiRNA Sponge Modules from heterogeneous data. To understand the miRNA sponging activities in biological conditions, LMSM uses gene expression data to evaluate the influence of the shared miRNAs on the clustered sponge lncRNAs and mRNAs. We have applied LMSM to the human breast cancer (BRCA) dataset from The Cancer Genome Atlas (TCGA). As a result, we have found that the majority of LMSM modules are significantly implicated in BRCA and most of them are BRCA subtype-specific. Most of the mediating miRNAs act as crosslinks across different LMSM modules, and all of LMSM modules are statistically significant. Multi-label classification analysis shows that the performance of LMSM modules is significantly higher than baseline’s performance, indicating the biological meanings of LMSM modules in classifying BRCA subtypes. The consistent results suggest that LMSM is robust in identifying lncRNA related miRNA sponge modules. Moreover, LMSM can be used to predict miRNA targets. Finally, LMSM outperforms a graph clustering-based strategy in identifying BRCA-related modules. Altogether, our study shows that LMSM is a promising method to investigate modular regulatory mechanism of sponge lncRNAs from heterogeneous data. Previous studies have revealed that long non-coding RNAs (lncRNAs), as microRNA (miRNA) sponges or competing endogenous RNAs (ceRNAs), can regulate the expression levels of messenger RNAs (mRNAs) by decreasing the amount of miRNAs interacting with mRNAs. In this work, we hypothesize that the “tug-of-war” between RNA transcripts for attracting miRNAs is across groups or modules. Based on the hypothesis, we propose a framework called LMSM, to identify LncRNA related MiRNA Sponge Modules. Based on the two miRNA sponge modular competition principles, significant sharing of miRNAs and high canonical correlation between the sponge lncRNAs and mRNAs, LMSM is also capable of predicting miRNA targets. LMSM not only extends the ceRNA hypothesis, but also provides a novel way to investigate the biological functions and modular mechanism of lncRNAs in breast cancer.
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50
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Donato L, Scimone C, Alibrandi S, Rinaldi C, Sidoti A, D’Angelo R. Transcriptome Analyses of lncRNAs in A2E-Stressed Retinal Epithelial Cells Unveil Advanced Links between Metabolic Impairments Related to Oxidative Stress and Retinitis Pigmentosa. Antioxidants (Basel) 2020; 9:E318. [PMID: 32326576 PMCID: PMC7222347 DOI: 10.3390/antiox9040318] [Citation(s) in RCA: 44] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 04/08/2020] [Accepted: 04/14/2020] [Indexed: 12/12/2022] Open
Abstract
: Long non-coding RNAs (lncRNAs) are untranslated transcripts which regulate many biological processes. Changes in lncRNA expression pattern are well-known related to various human disorders, such as ocular diseases. Among them, retinitis pigmentosa, one of the most heterogeneous inherited disorder, is strictly related to oxidative stress. However, little is known about regulative aspects able to link oxidative stress to etiopathogenesis of retinitis. Thus, we realized a total RNA-Seq experiment, analyzing human retinal pigment epithelium cells treated by the oxidant agent N-retinylidene-N-retinylethanolamine (A2E), considering three independent experimental groups (untreated control cells, cells treated for 3 h and cells treated for 6 h). Differentially expressed lncRNAs were filtered out, explored with specific tools and databases, and finally subjected to pathway analysis. We detected 3,3'-overlapping ncRNAs, 107 antisense, 24 sense-intronic, four sense-overlapping and 227 lincRNAs very differentially expressed throughout all considered time points. Analyzed lncRNAs could be involved in several biochemical pathways related to compromised response to oxidative stress, carbohydrate and lipid metabolism impairment, melanin biosynthetic process alteration, deficiency in cellular response to amino acid starvation, unbalanced regulation of cofactor metabolic process, all leading to retinal cell death. The explored lncRNAs could play a relevant role in retinitis pigmentosa etiopathogenesis, and seem to be the ideal candidate for novel molecular markers and therapeutic strategies.
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Affiliation(s)
- Luigi Donato
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, Division of Medical Biotechnologies and Preventive Medicine, University of Messina, 98125 Messina, Italy
- Department of Biomolecular Strategies, Genetics and Avant-Garde Therapies, I.E.ME.S.T., 90139 Palermo, Italy
| | - Concetta Scimone
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, Division of Medical Biotechnologies and Preventive Medicine, University of Messina, 98125 Messina, Italy
- Department of Biomolecular Strategies, Genetics and Avant-Garde Therapies, I.E.ME.S.T., 90139 Palermo, Italy
| | - Simona Alibrandi
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, Division of Medical Biotechnologies and Preventive Medicine, University of Messina, 98125 Messina, Italy
- Department of Chemical, Biological, Pharmaceutical and Environmental Sciences, University of Messina, 98125 Messina, Italy
| | - Carmela Rinaldi
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, Division of Medical Biotechnologies and Preventive Medicine, University of Messina, 98125 Messina, Italy
| | - Antonina Sidoti
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, Division of Medical Biotechnologies and Preventive Medicine, University of Messina, 98125 Messina, Italy
- Department of Biomolecular Strategies, Genetics and Avant-Garde Therapies, I.E.ME.S.T., 90139 Palermo, Italy
| | - Rosalia D’Angelo
- Department of Biomedical and Dental Sciences and Morphofunctional Imaging, Division of Medical Biotechnologies and Preventive Medicine, University of Messina, 98125 Messina, Italy
- Department of Biomolecular Strategies, Genetics and Avant-Garde Therapies, I.E.ME.S.T., 90139 Palermo, Italy
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